Progressive Trigger Data and Detection Model

ABSTRACT

A system, method, and computer-readable medium are disclosed for detecting malicious entity behavior and providing accurate indicator of behaviors indicating occurrence of malicious behavior. Data input as to the entity behavior is received and monitored from different sources. The entity behavior is monitored over time at time periods. Detection probability is determined at each time period, where the detection probability relates to malicious behavior and increases over time. A trigger indicator of behavior is provided if the detection probability reaches a threshold value.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates in general to the field of computers andsimilar technologies, and in particular to software utilized in thisfield. Still more particularly, it relates to a method, system andcomputer-usable medium for detecting entity behavior and triggeringindicator of behaviors.

Description of the Related Art

Users interact with physical, system, data, and services resources ofall kinds, as well as each other, on a daily basis. Each of theseinteractions, whether accidental or intended, poses some degree ofsecurity risk, depending on the behavior of the user. In particular, theactions of a formerly trusted user may become malicious as a result ofbeing subverted, compromised or radicalized due to any number ofinternal or external factors or stressors. For example, financialpressure, political idealism, irrational thoughts, or other influencesmay adversely affect a user's intent and/or behavior.

Anomaly detection methods monitor behavior and actions of an entity,such as a user, for anomalies or potential security breaches. Indicatorof behaviors are provided if such anomalies or breaches are detected.Oftentimes results of such solutions include many false positives. Suchindicator of behavior based detection methods typically are onedimensional, univariate solutions lacking in context, correlation, andcorroboration. Such methods can lack an underlying entity-centric datamodel, and use analytics based on raw data. Priority cannot be derivedwhen there is no differentiation of indicator of behaviors, andarbitrary risk levels.

Anomaly detection and other statistical methods are often only executedagainst a single measurement, otherwise known as univariate time series,and that measurement is often the final act in a data exfiltrationbehavior pattern. Actual exfiltration actions are only a small part ofthe total behavioral detection opportunities, and only actualexfiltration actions are measured, distinguishing legitimate frommalicious behavior becomes difficult.

SUMMARY OF THE INVENTION

In one embodiment the invention relates to a method for detecting entitybehavior, comprising: receiving structured or unstructured raw datainput from various sources related to actions of an entity; monitoringover time, the raw data input and associating the actions of the entityto particular time periods; determining a detection probability per eachtime period as to a triggering an indicator of behavior regarding amalicious activity, wherein the detection probability increases overtime; and providing a trigger indicator of behavior if the detectionprobability reaches a threshold value.

In another embodiment the invention relates to a system comprising: aprocessor; a data bus coupled to the processor; and a non-transitory,computer-readable storage medium embodying computer program code, thenon-transitory, computer-readable storage medium being coupled to thedata bus, the computer program code interacting with a plurality ofcomputer operations and comprising instructions executable by theprocessor and configured for: receiving structured or unstructured rawdata input from various sources related to actions of an entity;monitoring over time, the raw data input and associating the actions ofthe entity to particular time periods; determining a detectionprobability per each time period as to a triggering an indicator ofbehavior regarding a malicious activity, wherein the detectionprobability increases over time; and providing a trigger indicator ofbehavior if the detection probability reaches a threshold value.

In another embodiment the invention relates to a computer-readablestorage medium embodying computer program code, the computer programcode comprising: receiving structured or unstructured raw data inputfrom various sources related to actions of an entity; monitoring overtime, the raw data input and associating the actions of the entity toparticular time periods; determining a detection probability per eachtime period as to a triggering an indicator of behavior regarding amalicious activity, wherein the detection probability increases overtime; and providing a trigger indicator of behavior if the detectionprobability reaches a threshold value.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features and advantages made apparent to those skilled in theart by referencing the accompanying drawings. The use of the samereference number throughout the several figures designates a like orsimilar element.

FIG. 1 depicts an exemplary client computer in which the presentinvention may be implemented;

FIG. 2 is a simplified block diagram of an edge device;

FIG. 3 is a simplified block diagram of an endpoint agent;

FIG. 4 is a simplified block diagram of a security analytics system;

FIG. 5 is a simplified block diagram of a security analytics system;

FIGS. 6a and 6b show a simplified block diagram of an entity behaviorprofile (EBP) and a prepopulated EBP;

FIGS. 7a and 7b show a block diagram of a security analytics systemenvironment;

FIG. 8 is a simplified block diagram showing the mapping of an event toa security vulnerability scenario;

FIG. 9 is a simplified block diagram of the generation of a session anda corresponding session-based fingerprint;

FIG. 10 is simplified block diagram of process flows associated with theoperation of an entity behavior catalog (EBC) system;

FIG. 11 is a generalized flowchart of the performance of session-basedfingerprint generation operations;

FIG. 12 is a table showing components of an EBP;

FIG. 13 is an activities table showing analytic utility actionsoccurring during a session;

FIG. 14 shows a simplified block diagram of the components of a cyberkill chain associated with the performance of a security operation;

FIGS. 15a and 15b are a generalized flowchart of the performance of EBPdefinition and management operations;

FIG. 16 shows a functional block diagram of the operation of an EBCsystem;

FIGS. 17a and 17b are a simplified block diagram showing components ofan EBC system;

FIG. 18 is a simplified block diagram showing the mapping of entitybehaviors to a risk use case scenario;

FIG. 19 is a simplified block diagram of an EBC system environment;

FIG. 20 is a simplified block diagram of an EBC system used to modify anexisting, or generate a new EBP;

FIGS. 21a through 21c are a generalized flowchart of the performance ofEBP system operations to generate a new, or modify an existing, EBP foran associated entity;

FIGS. 22a through 22d are a generalized flowchart of the performance ofEBP system operations to generate a new, or modify an existing,prepopulated EBP; and

FIG. 23 is a generalized flowchart for detecting entity behavior andtriggering indicator of behaviors.

DETAILED DESCRIPTION

A method, system and computer-usable medium are disclosed for detectingentity behavior and triggering indicator of behaviors. Certain aspectsof the invention include an appreciation that the existence of anyentity, whether it is an individual user, a group of users, anorganization, a device, a system, a network, an account, a domain, anoperation, a process, a software application, or a service, representssome degree of security risk.

Various aspects of the invention likewise include an appreciation thatcertain non-user entities, such as computing, communication, andsurveillance devices can be a source for telemetry associated withcertain events and entity behaviors. Likewise, various aspects of theinvention include an appreciation that certain accounts may be global,spanning multiple devices, such as a domain-level account allowing anentity access to multiple systems. Certain aspects of the inventionlikewise include an appreciation that a particular account may be sharedby multiple entities.

Accordingly, certain aspects of the invention include an appreciationthat events or indicator of behaviors, by themselves, have a potentialto be legitimate or malicious, and are often indistinguishable fromnoise; however, events or indicator of behaviors coupled in a pattern orprobabilistically, a clearer picture is determined and more corroboratedand provides more accurate indicator of behaviors.

In certain embodiments described herein, a four layer computational datamodel is implemented, incorporating raw data, summary data, observables,and detections. Raw data is considered as a selected set of unaltereddata or filtered raw data that has been cleaned, transformed, andnormalized into a Common Information Model (CIM). A CIM can include a“user name” which for example is includes field names such as“UserName”, “user.name”, “user_name=user”, “logon_id”, “username”, etc.In certain implementations, the computational data model can include tworequirements or pre-requisites, entities (i.e., users and devices) andthe CIM. Cleaning refers to collecting only minimum necessary fields.Filtering refers to either whitelisting or excluded certain values.Transforming refers to changing field names to the CIM. Normalizingrefers to making field values the same as designated by the CIM.

Summary data can be considered as the aggregation of log data thatrepresents an entity's behavior over time. Summary data is a reduced setof raw data that is a more concise representation of behavior with aminimum quantity of key value (KV) pairs. Summary data can be used tocreate observables, displayed to an analyst, or used as inputs formachine learning (ML) algorithms. Summary data is derived from domainexpertise or the understanding of what data is needed for eventualdetections.

Observables are a set of events which can contain between “1” and “n”logs, or summary data that, by themselves, are generally not enoughevidence of a malicious event to require a response, since observablescan have a similar probability of being legitimate or malicious. Incertain instances, a single strong observable can be enough for adetection. Patterns of observables create detections. Observables can bederived from individual logs or summary data based on domain expertise.

Detections are sets of observables that would require a response. If adetection is triggered, either an automated (i.e., dynamic) or responsefrom an analyst is expected. A detection set can be from “1” to “n”observables. In certain implementations, detections are the smallestquantity in volume within a computational or progressive triggerframework as described herein.

The computational or progressive trigger framework provides fordetection, where detection is defied for a high probability of amalicious behavior by an entity that requires a response. In certainimplementations, detections can be place in a triaged order in a userinterface. High probability is defined as a high risk context thatexceeds a configured threshold. Risk context is a quantitative valueplus a textual explanation. Computed quantitative value can be anumerical value determined by methods, and particularly methods fordetermining detection probability. Examples of such methods includecontextual determination using simple mathematical aggregations withweighted factors and time/sequence triggers; Bayesian inference which isa numeric probability that a detection has occurred based on updatedevidence (e.g., prior probability, likelihoods, hypothesis,evidence->posterior probability>detection threshold, etc.); Markov Statewhich determines the probability of a detection given the current stateof the observable set, etc. Furthermore, combinations of such methodsand other decision theory algorithms can be implemented.

For the purposes of this disclosure, an information handling system mayinclude any instrumentality or aggregate of instrumentalities operableto compute, classify, process, transmit, receive, retrieve, originate,switch, store, display, manifest, detect, record, reproduce, handle, orutilize any form of information, intelligence, or data for business,scientific, control, entertainment, or other purposes. For example, aninformation handling system may be a personal computer, a mobile devicesuch as a tablet or smartphone, a consumer electronic device, aconnected “smart device,” a network appliance, a network storage device,a network gateway device, a server or collection of servers or any othersuitable device and may vary in size, shape, performance, functionality,and price. The information handling system may include volatile and/ornon-volatile memory, and one or more processing resources such as acentral processing unit (CPU) or hardware or software control logic.Additional components of the information handling system may include oneor more storage systems, one or more wired or wireless interfaces forcommunicating with other networked devices, external devices, andvarious input and output (I/O) devices, such as a keyboard, a mouse, amicrophone, speakers, a track pad, a touchscreen and a display device(including a touch sensitive display device). The information handlingsystem may also include one or more buses operable to transmitcommunication between the various hardware components.

For the purposes of this disclosure, computer-readable media may includeany instrumentality or aggregation of instrumentalities that may retaindata and/or instructions for a period of time. Computer-readable mediamay include, without limitation, storage media such as a direct accessstorage device (e.g., a hard disk drive or solid state drive), asequential access storage device (e.g., a tape disk drive), opticalstorage device, random access memory (RAM), read-only memory (ROM),electrically erasable programmable read-only memory (EEPROM), and/orflash memory; as well as communications media such as wires, opticalfibers, microwaves, radio waves, and other electromagnetic and/oroptical carriers; and/or any combination of the foregoing.

FIG. 1 is a generalized illustration of an information handling system100 that can be used to implement the system and method of the presentinvention. The information handling system 100 includes a processor(e.g., central processor unit or “CPU”) 102, input/output (I/O) devices104, such as a display, a keyboard, a mouse, and associated controllers,a storage system 106, and various other subsystems 108. In variousembodiments, the information handling system 100 also includes networkport 110 operable to connect to a network 140, which is likewiseaccessible by a service provider server 142. The information handlingsystem 100 likewise includes system memory 112, which is interconnectedto the foregoing via one or more buses 114. System memory 112 furtherincludes operating system (OS) 116 and in various embodiments may alsoinclude a security analytics system 118. In one embodiment, theinformation handling system 100 is able to download the securityanalytics system 118 from the service provider server 142. In anotherembodiment, the security analytics system 118 is provided as a servicefrom the service provider server 142.

In various embodiments, the security analytics system 118 performs asecurity analytics operation. In certain embodiments, the securityanalytics operation improves processor efficiency, and thus theefficiency of the information handling system 100, by facilitatingsecurity analytics functions. As will be appreciated, once theinformation handling system 100 is configured to perform the securityanalytics operation, the information handling system 100 becomes aspecialized computing device specifically configured to perform thesecurity analytics operation and is not a general purpose computingdevice. Moreover, the implementation of the security analytics system118 on the information handling system 100 improves the functionality ofthe information handling system 100 and provides a useful and concreteresult of performing security analytics functions to mitigate securityrisk.

In certain embodiments, the security analytics system 118 may beimplemented to include an entity behavior catalog (EBC) system 120 asfurther described herein. Certain embodiments further provide for thesecurity analytics system 118 include a computational or progressivetrigger framework system 122. In certain embodiments, the progressivetrigger framework system 122 is implemented to perform detecting entitybehavior and triggering indicator of behaviors. In various embodiments,the progressive trigger framework system 122 provides for multivariateand multidimensional analytics, machine learning, high-value behavioraldetections over time, and dynamic risk signals provided back to endpointdevices.

Certain embodiments provide for the progressive trigger framework system122 to include a primary analysis engine 124. The primary analysisengine 124 can be configured to receive structured or unstructured datainput (i.e., raw data) from various sources. In certain implementations,the data input (i.e., raw data) is from endpoint devices as describedherein. Data received can be received from various sources, includingsocial networks, network access, cloud access, etc. Furthermore, therecan be agnostic feature as to where the data is received from. Receivingdata from different sources, and agnostic as to the source of the data,can allow for more robust and high fidelity data to support the fourlayer computational data model.

In certain implementations, the primary analysis engine 124 performvarious functions as to the received data, such as querying raw data andwriting observables, querying raw data and writing summary data;querying summary data and writing observables; and querying observable,enrichment, and entity state to write detections. The primary analysisengine 124 can also be configured to send signals (dynamic risk signals)as to dynamic actions, dynamic data protection, dynamic user protection,etc. back to the data source (e.g., sending endpoint).

In certain implementations, processed data from the primary analysisengine 124 is communicated to a data stores, that can include storagesystem 106. Certain implementations provide for “1” to “n” data stores,summary data store, enrichment data store, observable and detection datastore, entity state data store, non-entity data store, etc.

Certain embodiments provide for the progressive trigger framework system122 to include a secondary machine language (ML) engine 126. In certainimplementations, the secondary ML engine 126 queries summary data forfeature vector values as further described herein. The secondary MLengine 126 further can train ML models offline, write observables formbatch ML classification results, and forward ML models for online MLclassification.

The progressive trigger framework system 122 further can be implementedto provide multivariate and multidimensional analytics and high valuebehavioral detections over time.

The following is an example of detecting entity behavior and triggeringindicator of behaviors. The example involves an entity that is leaving acompany. Typically, behaviors or actions of such an entity arelegitimate and there is no cause to provide an indicator of behavior tothe company. The entity or “leaver” may be looking at new jobopportunities, which typically is of little or no concern to a company.In certain cases, there can be some malicious intent by the “leaver.”Malicious behavior or actions of the “leaver” may include destroying orillegally taking data/information from the company. The methods andsystems described herein are directed to minimizing “false” indicator ofbehaviors that can be associated with legitimate behavior; however,provide indicator of behaviors when illegitimate behavior is detected.

As discussed, an entity can either be a user or device associated with auser. In the described computational data model, an entity (e.g., useror device) and the common information are implemented. The incidentresponse (i.e., alerting) concept of stacking data against an entity isimplemented. The computational data model is used to createhigh-fidelity detections by stacking observables of entity to match bothspecified and probabilistic detection patterns.

In typical methods, observed behavior of an entity is taken over time,such as at periods t₁, t₂, t₃, t₄, t₅ to t_(n). “Raw data” and “events”are monitored at each period. Examples of raw data can include uservisits to an external job, applying for a job, sending resignationletter, etc. Events can include anomalous increase in visits to the jobsite, events with keyword matches to “jobs”, “resignation”, etc., eventsof user notifying human resources, etc. Typically, false or nuisanceindicator of behaviors can be generated when such raw data and eventsoccur at particular time periods. Such methods could require anadditional action to determine if such indicator of behaviors are valid.

In the described progressive trigger approach, the example scenarioimplements monitoring of “raw data and summary data”, “leaver/entityobservables”, and “other observables” at periods t₁, t₂, t₃, t₄, t₅ tot_(n). For example, “raw data and summary data” at t₁ can be that“user's activities are normal.” “Raw data and summary data” at t₂ can bethat “user's summary data indicates an increase in job site activity.”User activities indicating the user viewing of job site, and the“leaver/entity observable” is observed from a URL summarization of thejob site. For example, at t₃ “raw data and summary data” can include“user views open jobs on the job site.” User activities indicating theuser is looking at jobs. A “leaver/entity observable” observed as a URLfuzzy match to the job site. Fuzzy matching may require knowledge ofkeywords, URL structure, and variations of the string field beingmatching for similarity. Progressively, this could mean near exactpattern matches for well-known sites or variables, or it could meantaking the URL structure of job sites, job applications, job viewing,resume posting, etc., and utilize deep learning to figure it out for us.This can be, progressively, a fairly simple analytic to a complexalgorithm, depending on the use case.

Progression takes place by monitoring at successive time periods up tot_(n). For example, eventually at t_(n)“raw data and summary data” is“user leaves the company.” At each time period t₁, t₂, t₃, t₄, t₅ tot_(n), a “detection probability” is assigned indicating whether toprovide a trigger or indicator of behavior. For example, 0% detectionprobability at t₁; y₁% detection probability at t₂; y₂% detectionprobability at t₃; y₃% detection probability at t₄; and y₄% detectionprobability at t₅; and y₁% detection probability at t. In this case, notrigger or detection (i.e., indicator of behavior, incident) is created;however, as time periods progress, the risk or detection probability canincrease, such that a detection or trigger can be created.

The same leaver and data theft scenario is described in a differentprogression. The progression as described above takes place with thesame monitored “raw data and summary data” and “leaver/entityobservables” monitored at periods at periods t₁, t₂, t₃, t₄, t₅ tot_(n). In the described progressive trigger approach, “otherobservables” are also monitored. At “raw data and summary data” at t₅ is“user sends resignation notice by email” indicating an activity that“user resigns from employment. The “leaver/entity observables” at t₅ isobserved from an email scrape fuzzy or natural language match to“resignation letter”. In this progression, at period t₅ the “otherobservables' is “user observed uploading data (company) to a USB drivefor the first time.” In most cases, such an observable may be routineand legitimate; however, in this case, taking into account that theentity has submitted a resignation letter and now is downloading companydata, an indicator of behavior should be triggered. Detection probablyy₄% at t₅ is increased. A detection should be triggered.

The same leaver and data theft scenario is described in a differentprogression. In this progression, “other observables” of the “userobserved uploading data (company) to a USB drive” which may be “for thefirst time,” is seen at time (period) before t₅. For example, this“other observables” taking place sometime between periods t₂ and t₄.Should an indicator of behavior be triggered? In certainimplementations, a threshold can be set as to the detection probabilityat that point. As discussed, over time the detection probability (y_(n))can increase. If the detection probability (y_(n)) reaches a setthreshold value, an indicator of behavior can be triggered at aparticular time. Therefore, if there are different “other observables”that could potentially trigger an indicator of behavior, a trigger pointsetting is determined based on a set threshold value for the detectionprobability (y_(n)).

In certain implementations, the detection probability (y_(n)) can be theresult of one of many probabilistic techniques, such as a Bayesianbelief network, Markov state, exact pattern match, etc. Furthermore, incertain implementations, a “state” can be indicated that the detectionprobability (y_(n)) relates to a higher risk, but a decision to triggeran indicator of behavior has not occurred.

Certain implementations provide for an endpoint device or theprogressive trigger framework system 122 of the information handlingsystem 100 to monitor “leaver/entity observables” and “otherobservables.” Certain implementations provide for the trigger frameworksystem 122 to determine detection probability (y_(n)).

In determining entity or user behavior, “summary data” of “raw data andsummary data” can be representative over time and baselined to come upwith “normal user” behavior. The use of specified behavior patterns canbe implemented; however, if such behavior patterns are unknown,probabilistic behavior patterns can be implemented.

When components in probabilistic behavior patterns are unknown, phasestacking can be used. Phases can be defined as stages in a chain of datatheft or data theft kill chain. For example, a data theft kill chain caninclude the following phases in order: initial access, execution,persistence, privilege escalation, defense evasion, credential access,discovery, lateral movement, collection, command & control, exfiltrationand impact. In phase stacking, each phase is converted to a numericalvalue in a feature vector of predicators. For example, the featurevector is x=[x_(initial), x_(access), x_(execution), x_(persistence),x_(privilege escalation), x_(defense evasion), x_(credential access),x_(discovery), x_(lateral movement), x_(collection),x_(command & control), x_(exfiltration), x_(impact)]. A weightingfunction can be sued to populate the feature vector, where the valuesare between 0 and 1. An example can be x=[0, 0, 0, 0, 0.8, 0.9, 0.5, 0,0, 0.8, 0]. The weighted vector can be used for inputs to predict aprobability of an unspecified behavior pattern match.

FIG. 2 is a simplified block diagram of an edge device implemented inaccordance with an embodiment of the invention. As used herein, an edgedevice, such as the edge device 202 shown in FIG. 2, broadly refers to adevice providing an entry point into a network 140. Examples of suchedge devices 202 may include routers, routing switches, integratedaccess devices (IADs), multiplexers, wide-area network (WAN) accessdevices, and network security appliances. In certain embodiments, thenetwork 140 may be a private network (e.g., an enterprise network), asemi-public network (e.g., a service provider core network), or a publicnetwork (e.g., the Internet).

Skilled practitioners of the art will be aware that edge devices 202 areoften implemented as routers that provide authenticated access tofaster, more efficient backbone and core networks. Furthermore, currentindustry trends include making edge devices 202 more intelligent, whichallows core devices to operate at higher speed as they are not burdenedwith additional administrative overhead. Accordingly, such edge devices202 often include Quality of Service (QoS) and multi-service functionsto manage different types of traffic. Consequently, it is common todesign core networks with switches that use routing protocols such asOpen Shortest Path First (OSPF) or Multiprotocol Label Switching (MPLS)for reliability and scalability. Such approaches allow edge devices 202to have redundant links to the core network, which not only providesimproved reliability, but enables enhanced, flexible, and scalablesecurity capabilities as well.

In certain embodiments, the edge device 202 may be implemented toinclude a communications/services architecture 204, various pluggablecapabilities 212, a traffic router 210, and a pluggable hostingframework 208. In certain embodiments, the communications/servicesarchitecture 202 may be implemented to provide access to and fromvarious networks 140, cloud services 206, or a combination thereof. Incertain embodiments, the cloud services 206 may be provided by a cloudinfrastructure familiar to those of skill in the art. In certainembodiments, the edge device 202 may be implemented to provide supportfor a variety of generic services, such as directory integration,logging interfaces, update services, and bidirectional risk/contextflows associated with various analytics. In certain embodiments, theedge device 202 may be implemented to provide temporal information,described in greater detail herein, associated with the provision ofsuch services.

In certain embodiments, the edge device 202 may be implemented as ageneric device configured to host various network communications, dataprocessing, and security management capabilities. In certainembodiments, the pluggable hosting framework 208 may be implemented tohost such capabilities in the form of pluggable capabilities 212. Incertain embodiments, the pluggable capabilities 212 may includecapability ‘1’ 214 (e.g., basic firewall), capability ‘2’ 216 (e.g.,general web protection), capability ‘3’ 218 (e.g., data sanitization),and so forth through capability ‘n’ 220, which may include capabilitiesneeded for a particular operation, process, or requirement on anas-needed basis. In certain embodiments, such capabilities may includethe performance of operations associated with managing an adaptive trustProfile (ATP), described in greater detail herein. In certainembodiments, such operations may include the provision of associatedtemporal information (e.g., time stamps).

In certain embodiments, the pluggable capabilities 212 may be sourcedfrom various cloud services 206. In certain embodiments, the pluggablehosting framework 208 may be implemented to provide certain computingand communication infrastructure components, and foundationcapabilities, required by one or more of the pluggable capabilities 212.In certain embodiments, the pluggable hosting framework 208 may beimplemented to allow the pluggable capabilities 212 to be dynamicallyinvoked. Skilled practitioners of the art will recognize that many suchembodiments are possible. Accordingly, the foregoing is not intended tolimit the spirit, scope or intent of the invention.

FIG. 3 is a simplified block diagram of an endpoint agent implemented inaccordance with an embodiment of the invention. As used herein, anendpoint agent 306 broadly refers to a software agent used incombination with an endpoint device 304 to establish a protectedendpoint 302. Skilled practitioners of the art will be familiar withsoftware agents, which are computer programs that perform actions onbehalf of a user or another program. In various approaches, a softwareagent may be autonomous or work together with another agent or a user.In certain of these approaches the software agent is implemented toautonomously decide if a particular action is appropriate for a givenevent, such as an observed entity behavior.

As implemented in detecting entity behavior and triggering indicator ofbehaviors, protected endpoint 302 provides for data ingest andforwarding; counters, rules, enrichment, and indicator of behaviors;endpoint-appropriate analytics; policy enforcement; and rich datasets.

An endpoint device 304, as likewise used herein, refers to aninformation processing system such as a personal computer, a laptopcomputer, a tablet computer, a personal digital assistant (PDA), a smartphone, a mobile telephone, a digital camera, a video camera, or otherdevice capable of storing, processing and communicating data. In certainembodiments, the communication of the data may take place in real-timeor near-real-time. As used herein, real-time broadly refers toprocessing and providing information within a time interval brief enoughto not be discernable by a user. As an example, a cellular phoneconversation may be used to communicate information in real-time, whilean instant message (IM) exchange may be used to communicate informationin near real-time. In certain embodiments, the communication of theinformation may take place asynchronously. For example, an email messagemay be stored on an endpoint device 304 when it is offline. In thisexample, the information may be communicated to its intended recipientonce the endpoint device 304 gains access to a network 140.

A protected endpoint 302, as likewise used herein, broadly refers to apolicy-based approach to network security that typically requiresendpoint devices 304 to comply with certain criteria before they aregranted access to network resources. As an example, a given endpointdevice 304 may be required to have a particular operating system (OS),or version thereof, a Virtual Private Network (VPN) client, anti-virussoftware with current updates, and so forth. In certain embodiments, theprotected endpoint 302 may be implemented to perform operationsassociated with providing real-time resolution of the identity of anentity at a particular point in time, as described in greater detailherein. In certain embodiments, the protected endpoint 302 may beimplemented to provide temporal information, such as timestampinformation, associated with such operations.

In certain embodiments, the real-time resolution of the identity of anentity at a particular point in time may be based upon contextualinformation associated with a given entity behavior. As used herein,contextual information broadly refers to any information, directly orindirectly, individually or in combination, related to a particularentity behavior. In certain embodiments, entity behavior may include anentity's physical behavior, cyber behavior, or a combination thereof. Aslikewise used herein, physical behavior broadly refers to any entitybehavior occurring within a physical realm. More particularly, physicalbehavior may include any action enacted by an entity that can beobjectively observed, or indirectly inferred, within a physical realm.

As an example, a user may attempt to use an electronic access card toenter a secured building at a certain time. In this example, the use ofthe access card to enter the building is the action and the reading ofthe access card makes the user's physical behaviorelectronically-observable. As another example, a first user mayphysically transfer a document to a second user, which is captured by avideo surveillance system. In this example, the physical transferal ofthe document from the first user to the second user is the action.Likewise, the video record of the transferal makes the first and seconduser's physical behavior electronically-observable. As used herein,electronically-observable entity behavior broadly refers to any behaviorexhibited or enacted by an entity that can be electronically observed.

Cyber behavior, as used herein, broadly refers to any behavior occurringin cyberspace, whether enacted by an individual user, a group of users,or a system acting at the behest of an individual user, a group ofusers, or an entity. More particularly, cyber behavior may includephysical, social, or mental actions that can be objectively observed, orindirectly inferred, within cyberspace. As an example, a user may use anendpoint device 304 to access and browse a particular website on theInternet. In this example, the individual actions performed by the userto access and browse the website constitute a cyber behavior. As anotherexample, a user may use an endpoint device 304 to download a data filefrom a particular system at a particular point in time. In this example,the individual actions performed by the user to download the data file,and associated temporal information, such as a time-stamp associatedwith the download, constitute a cyber behavior. In these examples, theactions are enacted within cyberspace, in combination with associatedtemporal information, which makes them electronically-observable.

As likewise used herein, cyberspace broadly refers to a network 140environment capable of supporting communication between two or moreentities. In certain embodiments, the entity may be a user, an endpointdevice 304, or various resources, described in greater detail herein. Incertain embodiments, the entities may include various endpoint devices304 or resources operating at the behest of an entity, such as a user.In certain embodiments, the communication between the entities mayinclude audio, image, video, text, or binary data.

As likewise used herein, an entity broadly refers to something thatexists as itself, whether physically or abstractly. In certainembodiments, an entity may be an individual user, a group, anorganization, or a government. In certain embodiments, an entity maylikewise be an item, a device, such as endpoint and edge devices, anetwork, a domain, an operation, or a process. In certain embodiments,an entity may be a resource, such as a geographical location orformation, a physical facility, a venue, a system, a data store, or aservice, such as a service operating in a cloud environment.

As described in greater detail herein, the contextual information mayinclude a user's authentication factors. Contextual information maylikewise include various temporal identity resolution factors, such asidentification factors associated with the entity, thedate/time/frequency of various entity behaviors, the entity's location,the entity's role or position in an organization, their associatedaccess rights, and certain user gestures employed by a user in theenactment of a user behavior. Other contextual information may likewiseinclude various user interactions, whether the interactions are with anendpoint device 304, a network 140, a resource, or another user. Incertain embodiments, entity behaviors, and their related contextualinformation, may be collected at particular points of observation, andat particular points in time, described in greater detail herein. Incertain embodiments, a protected endpoint 302 may be implemented as apoint of observation for the collection of entity behavior andcontextual information.

In certain embodiments, the endpoint agent 306 may be implemented touniversally support a variety of operating systems, such as AppleMacintosh®, Microsoft Windows®, Linux®, Android® and so forth. Incertain embodiments, the endpoint agent 306 may be implemented tointeract with the endpoint device 304 through the use of low-level hooks312 at the operating system level. It will be appreciated that the useof low-level hooks 312 allows the endpoint agent 306 to subscribe tomultiple events through a single hook. Consequently, multiplefunctionalities provided by the endpoint agent 306 can share a singledata stream, using only those portions of the data stream they mayindividually need. Accordingly, system efficiency can be improved andoperational overhead reduced.

In certain embodiments, the endpoint agent 306 may be implemented toprovide a common infrastructure for pluggable feature packs 308. Invarious embodiments, the pluggable feature packs 308 may provide certainsecurity management functionalities. Examples of such functionalitiesmay include various anti-virus and malware detection, data lossprotection (DLP), insider threat detection, and so forth. In certainembodiments, the security management functionalities may include one ormore functionalities associated with providing real-time resolution ofthe identity of an entity at a particular point in time, as described ingreater detail herein.

In certain embodiments, a particular pluggable feature pack 308 isinvoked as needed by the endpoint agent 306 to provide a givenfunctionality. In certain embodiments, individual features of aparticular pluggable feature pack 308 are invoked as needed. It will beappreciated that the ability to invoke individual features of apluggable feature pack 308, without necessarily invoking all suchfeatures, will likely improve the operational efficiency of the endpointagent 306 while simultaneously reducing operational overhead.Accordingly, the endpoint agent 306 can self-optimize in certainembodiments by using the common infrastructure and invoking only thosepluggable components that are applicable or needed for a given userbehavior.

In certain embodiments, the individual features of a pluggable featurepack 308 are invoked by the endpoint agent 306 according to theoccurrence of a particular user behavior. In certain embodiments, theindividual features of a pluggable feature pack 308 are invoked by theendpoint agent 306 according to the occurrence of a particular temporalevent, described in greater detail herein. In certain embodiments, theindividual features of a pluggable feature pack 308 are invoked by theendpoint agent 306 at a particular point in time. In these embodiments,the method by which a given user behavior, temporal event, or point intime is selected is a matter of design choice.

In certain embodiments, the individual features of a pluggable featurepack 308 may be invoked by the endpoint agent 306 according to thecontext of a particular user behavior. As an example, the context may bethe user enacting the user behavior, their associated riskclassification, which resource they may be requesting, the point in timethe user behavior is enacted, and so forth. In certain embodiments, thepluggable feature packs 308 may be sourced from various cloud services206. In certain embodiments, the pluggable feature packs 308 may bedynamically sourced from various cloud services 206 by the endpointagent 306 on an as-need basis.

In certain embodiments, the endpoint agent 306 may be implemented withadditional functionalities, such as event analytics 310. In certainembodiments, the event analytics 310 functionality may include analysisof various user behaviors, described in greater detail herein. Incertain embodiments, the endpoint agent 306 may be implemented with athin hypervisor 314, which can be run at Ring −1, thereby providingprotection for the endpoint agent 306 in the event of a breach. As usedherein, a thin hypervisor broadly refers to a simplified, OS-dependenthypervisor implemented to increase security. As likewise used herein,Ring −1 broadly refers to approaches allowing guest operating systems torun Ring 0 (i.e., kernel) operations without affecting other guests orthe host OS. Those of skill in the art will recognize that many suchembodiments and examples are possible. Accordingly, the foregoing is notintended to limit the spirit, scope or intent of the invention.

FIG. 4 is a simplified block diagram of a security analytics systemimplemented in accordance with an embodiment of the invention. Incertain embodiments, the security analytics system 118 shown in FIG. 4may include an event queue analytics 404 module, described in greaterdetail herein. In certain embodiments, the event queue analytics 404sub-system may be implemented to include an enrichment 406 module and astreaming analytics 408 module. In certain embodiments, the securityanalytics system 118 may be implemented to provide log storage,reporting, and analytics capable of performing streaming 408 andon-demand 410 analytics operations. In certain embodiments, suchoperations may be associated with defining and managing an adaptivetrust profile (ATP), detecting entity behavior that may be of analyticutility, adaptively responding to mitigate risk, or a combinationthereof, as described in greater detail herein. In certain embodiments,entity behavior of analytic utility may be determined to be anomalous,abnormal, unexpected, malicious, or some combination thereof, asdescribed in greater detail herein.

In certain embodiments, the security analytics system 118 may beimplemented to provide a uniform platform for storing events andcontextual information associated with various entity behaviors andperforming longitudinal analytics. As used herein, longitudinalanalytics broadly refers to performing analytics of entity behaviorsoccurring over a particular period of time. As an example, an entity mayiteratively attempt to access certain proprietary information stored invarious locations. In addition, the attempts may occur over a briefperiod of time. To continue the example, the fact that the informationthe entity is attempting to access is proprietary, that it is stored invarious locations, and the attempts are occurring in a brief period oftime, in combination, may indicate the entity behavior enacted by theentity is suspicious. As another example, certain entity identifierinformation (e.g., a user name) associated with an entity may changeover time. In this example, a change in the entity's user name, during aparticular period of time or at a particular point in time, mayrepresent suspicious entity behavior.

In certain embodiments, the security analytics system 118 may beimplemented to be scalable. In certain embodiments, the securityanalytics system 118 may be implemented in a centralized location, suchas a corporate data center. In these embodiments, additional resourcesmay be added to the security analytics system 118 as needs grow. Incertain embodiments, the security analytics system 118 may beimplemented as a distributed system. In these embodiments, the securityanalytics system 118 may span multiple information handling systems. Incertain embodiments, the security analytics system 118 may beimplemented in a cloud environment. In certain embodiments, the securityanalytics system 118 may be implemented in a virtual machine (VM)environment. In such embodiments, the VM environment may be configuredto dynamically and seamlessly scale the security analytics system 118 asneeded. Skilled practitioners of the art will recognize that many suchembodiments are possible. Accordingly, the foregoing is not intended tolimit the spirit, scope or intent of the invention.

In certain embodiments, an event stream collector 402 may be implementedto collect event and related contextual information, described ingreater detail herein, associated with various entity behaviors. Inthese embodiments, the method by which the event and contextualinformation is selected to be collected by the event stream collector402 is a matter of design choice. In certain embodiments, the event andcontextual information collected by the event stream collector 402 maybe processed by an enrichment module 406 to generate enriched entitybehavior information. In certain embodiments, the enrichment may includecertain contextual information related to a particular entity behavioror event. In certain embodiments, the enrichment may include certaintemporal information, such as timestamp information, related to aparticular entity behavior or event.

In certain embodiments, enriched entity behavior information may beprovided by the enrichment module 406 to a streaming 408 analyticsmodule. In turn, the streaming 408 analytics module may provide some orall of the enriched entity behavior information to an on-demand 410analytics module. As used herein, streaming 408 analytics broadly refersto analytics performed in near real-time on enriched entity behaviorinformation as it is received. Likewise, on-demand 410 analytics broadlyrefers herein to analytics performed, as they are requested, on enrichedentity behavior information after it has been received. In certainembodiments, the enriched entity behavior information may be associatedwith a particular event. In certain embodiments, the enrichment 406 andstreaming analytics 408 modules may be implemented to perform eventqueue analytics 404 operations, as described in greater detail herein.

In certain embodiments, the on-demand 410 analytics may be performed onenriched entity behavior associated with a particular interval of, orpoint in, time. In certain embodiments, the streaming 408 or on-demand410 analytics may be performed on enriched entity behavior associatedwith a particular user, group of users, one or more non-user entities,or a combination thereof. In certain embodiments, the streaming 408 oron-demand 410 analytics may be performed on enriched entity behaviorassociated with a particular resource, such as a facility, system,datastore, or service. Those of skill in the art will recognize thatmany such embodiments are possible. Accordingly, the foregoing is notintended to limit the spirit, scope or intent of the invention.

In certain embodiments, the results of various analytics operationsperformed by the streaming 408 or on-demand 410 analytics modules may beprovided to a storage Application Program Interface (API) 414. In turn,the storage API 412 may be implemented to provide access to variousdatastores ‘1’ 416 through ‘n’ 418, which in turn are used to store theresults of the analytics operations. In certain embodiments, thesecurity analytics system 118 may be implemented with a logging andreporting front-end 412, which is used to receive the results ofanalytics operations performed by the streaming 408 analytics module. Incertain embodiments, the datastores ‘1’ 416 through ‘n’ 418 mayvariously include a datastore of entity identifiers, temporal events, ora combination thereof.

In certain embodiments, the security analytics system 118 may include arisk scoring 420 module implemented to perform risk scoring operations,described in greater detail herein. In certain embodiments,functionalities of the risk scoring 420 module may be provided in theform of a risk management service 422. In certain embodiments, the riskmanagement service 422 may be implemented to perform operationsassociated with defining and managing an adaptive trust profile (ATP),as described in greater detail herein. In certain embodiments, the riskmanagement service 422 may be implemented to perform operationsassociated with detecting entity behavior that may be of analyticutility and adaptively responding to mitigate risk, as described ingreater detail herein. In certain embodiments, the risk managementservice 422 may be implemented to provide the results of variousanalytics operations performed by the streaming 406 or on-demand 408analytics modules. In certain embodiments, the risk management service422 may be implemented to use the storage API 412 to access variousenhanced cyber behavior and analytics information stored on thedatastores ‘1’ 414 through ‘n’ 416. Skilled practitioners of the artwill recognize that many such embodiments are possible. Accordingly, theforegoing is not intended to limit the spirit, scope or intent of theinvention.

FIG. 5 is a simplified block diagram of the operation of a securityanalytics system implemented in accordance with an embodiment of theinvention. In certain embodiments, the security analytics system 118 maybe implemented to perform operations associated with detecting entitybehavior that may be of analytic utility, as described in greater detailherein. In certain embodiments, the security analytics system 118 may beimplemented in combination with one or more endpoint agents 306, one ormore edge devices 202, various cloud services 206, and a network 140 toperform such operations.

In certain embodiments, the network edge device 202 may be implementedin a bridge, a firewall, or a passive monitoring configuration. Incertain embodiments, the edge device 202 may be implemented as softwarerunning on an information handling system. In certain embodiments, thenetwork edge device 202 may be implemented to provide integratedlogging, updating and control. In certain embodiments, the edge device202 may be implemented to receive network requests and context-sensitiveuser behavior information in the form of enriched user behaviorinformation 510, described in greater detail herein, from an endpointagent 306, likewise described in greater detail herein.

In certain embodiments, the security analytics system 118 may beimplemented as both a source and a sink of user behavior information. Incertain embodiments, the security analytics system 118 may beimplemented to serve requests for user/resource risk data. In certainembodiments, the edge device 202 and the endpoint agent 306,individually or in combination, may provide certain entity behaviorinformation to the security analytics system 118 using either push orpull approaches familiar to skilled practitioners of the art.

As described in greater detail herein, the edge device 202 may beimplemented in certain embodiments to receive enriched user behaviorinformation 510 from the endpoint agent 306. It will be appreciated thatsuch enriched user behavior information 510 will likely not be availablefor provision to the edge device 202 when an endpoint agent 306 is notimplemented for a corresponding endpoint device 304. However, the lackof such enriched user behavior information 510 may be accommodated invarious embodiments, albeit with reduced functionality related tooperations associated with defining and managing an entity profile,detecting entity behavior that may be normal or of analytic utility,mitigating associated risk, or a combination thereof.

In certain embodiments, a given user behavior may be enriched by anassociated endpoint agent 306 attaching contextual information to arequest. In certain embodiments, the context is embedded within anetwork request, which is then provided as enriched user behaviorinformation 510. In certain embodiments, the contextual information maybe concatenated, or appended, to a request, which in turn may beprovided as enriched user behavior information 510. In theseembodiments, the enriched user behavior information 510 may be unpackedupon receipt and parsed to separate the request and its associatedcontextual information. Certain embodiments of the invention reflect anappreciation that one possible disadvantage of such an approach is thatit may perturb certain Intrusion Detection System and/or IntrusionDetection Prevention (IDS/IDP) systems implemented on a network 140.

In certain embodiments, new flow requests may be accompanied by acontextual information packet sent to the edge device 202. In theseembodiments, the new flow requests may be provided as enriched userbehavior information 510. In certain embodiments, the endpoint agent 306may also send updated contextual information to the edge device 202 onceit becomes available. As an example, an endpoint agent 306 may share alist of files that have been read by a current process at any point intime once the information has been collected. To continue the example,such a list of files may be used to determine which data the endpointagent 306 may be attempting to exfiltrate.

In certain embodiments, point analytics processes executing on the edgedevice 202 may request a particular service. As an example, risk scoresassociated with a particular event on a per-user basis may be requested.In certain embodiments, the service may be requested from the securityanalytics system 118. In certain embodiments, the service may berequested from various cloud services 206.

In certain embodiments, contextual information associated with aparticular entity behavior may be attached to various network servicerequests. In certain embodiments, the request may be wrapped and thenhandled by proxy. In certain embodiments, a small packet of contextualinformation associated with an entity behavior may be sent with aservice request. In certain embodiments, service requests may be relatedto Domain Name Service (DNS), web browsing activity, email, and soforth, all of which are essentially requests for service by an endpointdevice 304. In certain embodiments, such service requests may beassociated with temporal event information, described in greater detailherein. Consequently, such requests can be enriched by the addition ofentity behavior contextual information (e.g., UserAccount,interactive/automated, data-touched, temporal event information, etc.).Accordingly, the edge device 202 can then use this information to managethe appropriate response to submitted requests.

In certain embodiments, the security analytics system 118 may beimplemented in different operational configurations. In certainembodiments, the security analytics system 118 may be implemented byusing the endpoint agent 306. In certain embodiments, the securityanalytics system 118 may be implemented by using endpoint agent 306 incombination with the edge device 202. In certain embodiments, the cloudservices 206 may likewise be implemented for use by the endpoint agent306, the edge device 202, and the security analytics system 118,individually or in combination. In these embodiments, the securityanalytics system 118 may be primarily oriented to performing riskassessment operations related to entity actions, software programactions, data accesses, or a combination thereof. In certainembodiments, software program actions may be treated as a proxy for theentity.

In certain embodiments, the endpoint agent 306 may be implemented toupdate the security analytics system 118 with user behavior andassociated contextual information, thereby allowing an offload ofcertain analytics processing overhead. In certain embodiments, thisapproach allows for longitudinal risk scoring, which assesses riskassociated with certain user behavior during a particular interval oftime. In certain embodiments, the security analytics system 118 may beimplemented to access risk scores associated with the same user account,but accrued on different endpoint devices 304. It will be appreciatedthat such an approach may prove advantageous when an adversary is“moving sideways” through a network environment, using differentendpoint devices 304 to collect information.

In certain embodiments, the security analytics system 118 may beprimarily oriented to applying risk mitigations in a way that maximizessecurity effort return-on-investment (ROI). In certain embodiments, thisapproach may be accomplished by providing additional contextual andentity behavior information associated with entity requests. As anexample, a web gateway may not concern itself with why a particular fileis being requested by a certain entity at a particular point in time.Accordingly, if the file cannot be identified as malicious or harmless,there is no context available to determine how, or if, to proceed. Toextend the example, the edge device 202 and security analytics system118 may be coupled such that requests can be contextualized and fittedinto a framework that evaluates their associated risk. Certainembodiments of the invention reflect an appreciation that such anapproach works well with web-based data loss protection (DLP)approaches, as each transfer is no longer examined in isolation, but inthe broader context of an identified entity's actions, at a particulartime, on the network 140.

As another example, the security analytics system 118 may be implementedto perform risk scoring processes to decide whether to block or allowunusual flows. In various embodiments, the risk scoring processes may beimplemented to include certain aspects of eXtensible Access ControlMarkup Language (XACML) approaches known to skilled practitioners of theart. In certain embodiments, XACML obligations may be implemented toblock or allow unusual flows. In certain embodiments, an XACMLobligation may be implemented as a directive from a policy decisionpoint (PDP) to a policy enforcement point (PEP) regarding what must beperformed before or after a flow is approved. Certain embodiments of theinvention reflect an appreciation that such an approach is highlyapplicable to defending against point-of-sale (POS) malware, a breachtechnique that has become increasingly more common in recent years.Certain embodiments of the invention likewise reflect an appreciationthat while various edge device 202 implementations may not stop all suchexfiltrations, they may be able to.

In certain embodiments, the security analytics system 118 may beprimarily oriented to maximally leverage contextual informationassociated with various entity behaviors within the system. In certainembodiments, data flow tracking is performed by one or more endpointagents 306, which allows the quantity and type of information associatedwith particular hosts to be measured. In turn, this information may beused to determine how the edge device 202 handles requests. Bycontextualizing such entity behavior on the network 140, the securityanalytics system 118 can provide intelligent protection, makingdecisions that make sense in the broader context of an organization'sactivities. Certain embodiments of the invention reflect an appreciationthat one advantage to such an approach is that information flowingthrough an organization, and the networks they employ, should betrackable, and substantial data breaches preventable. Skilledpractitioners of the art will recognize that many such embodiments andexamples are possible. Accordingly, the foregoing is not intended tolimit the spirit, scope or intent of the invention.

FIGS. 6a and 6b show a simplified block diagram of an entity behaviorprofile (EBP) and a prepopulated EBP entity behavior profile implementedin accordance with an embodiment of the invention. As used herein, anentity behavior profile 638 broadly refers to a collection ofinformation that uniquely describes a particular entity's identity andtheir associated behavior, whether the behavior occurs within a physicalrealm or cyberspace. In certain embodiments, an EBP 638 may be used toadaptively draw inferences regarding the trustworthiness of a particularentity. In certain embodiments, as described in greater detail herein,the drawing of the inferences may involve comparing a new entitybehavior to known past behaviors enacted by the entity. In certainembodiments, new entity behavior of analytic utility may represententity behavior that represents a security risk. As likewise usedherein, an entity broadly refers to something that exists as itself,whether physically or abstractly. In certain embodiments, an entity maybe a user entity, a non-user entity, or a combination thereof. Incertain embodiments, the identity of an entity may be known or unknown.

As used herein, a user entity broadly refers to an entity capable ofenacting a user entity behavior, as described in greater detail herein.Examples of a user entity include an individual person, a group ofpeople, an organization, or a government. As likewise used herein, anon-user entity broadly refers to an entity whose identity can bedescribed and may exhibit certain behavior, but is incapable of enactinga user entity behavior. Examples of a non-user entity include an item, adevice, such as endpoint and edge devices, a network, an account, adomain, an operation, a process, and an event. Other examples of anon-user entity include a resource, such as a geographical location orformation, a physical facility, a venue, a system, a softwareapplication, a data store, and a service, such as a service operating ina cloud environment.

Certain embodiments of the invention reflect an appreciation that beingable to uniquely identity a device may assist in establishing whether ornot a particular login is legitimate. As an example, user impersonationsmay not occur at the user's endpoint, but instead, from another deviceor system. Certain embodiments of the invention likewise reflect anappreciation that profiling the entity behavior of a particular deviceor system may assist in determining whether or not it is actingsuspiciously.

In certain embodiments, an account may be local account, which runs on asingle machine. In certain embodiments, an account may be a globalaccount, providing access to multiple resources. In certain embodiments,a process may be implemented to run in an unattended mode, such as whenbacking up files or checking for software updates. Certain embodimentsof the invention reflect an appreciation that it is often advantageousto track events at the process level as a method of determining whichevents are associated with background processes and which are initiatedby a user entity.

In certain embodiments, an EBP 638 may be implemented to include a userentity profile 602, an associated user entity mindset profile 630, anon-user entity profile 632, and an entity state 636. As used herein, auser entity profile 602 broadly refers to a collection of informationthat uniquely describes a user entity's identity and their associatedbehavior, whether the behavior occurs within a physical realm orcyberspace. In certain embodiments, as described in greater detailherein, the user entity profile 602 may include user profile attributes604, user behavior factors 610, user mindset factors 622, or acombination thereof. In certain embodiments, the user profile attributes604 may include certain user authentication factors 606, described ingreater detail herein, and personal information 608.

As used herein, a user profile attribute 604 broadly refers to data ormetadata that can be used, individually or in combination with otheruser profile attributes 604, user behavior factors 610, or user mindsetfactors 622, to ascertain the identity of a user entity. In variousembodiments, certain user profile attributes 604 may be uniquelyassociated with a particular user entity. In certain embodiments, thepersonal information 608 may include non-sensitive personal informationassociated with a user entity, such as their name, title, position,role, and responsibilities. In certain embodiments, the personalinformation 608 may likewise include technical skill level information,peer information, expense account information, paid time off (PTO)information, data analysis information, insider information,misconfiguration information, third party information, or a combinationthereof. In certain embodiments, the personal information 608 maycontain sensitive personal information associated with a user entity. Asused herein, sensitive personal information (SPI), also commonlyreferred to as personally identifiable information (PII), broadly refersto any information usable to ascertain the identity of a user entity,either by itself, or in combination with other information, such ascontextual information described in greater detail herein.

Examples of SPI may include the full or legal name of a user entity,initials or nicknames, place and date of birth, home and businessaddresses, personal and business telephone numbers, their gender, andother genetic information. Additional examples of SPI may includegovernment-issued identifiers, such as a Social Security Number (SSN) ora passport number, vehicle registration plate and serial numbers, anddriver's license numbers. Other examples of SPI may include certainemail addresses and social media identifiers, credit and debit cardnumbers, and other digital identity information. Yet other examples ofSPI may include employer-issued identifiers, financial transactioninformation, credit scores, electronic medical records (EMRs), insuranceclaim information, personal correspondence, and so forth. Furtherexamples of SPI may include user authentication factors 606, such asbiometrics, user identifiers and passwords, and personal identificationnumbers (PINs).

In certain embodiments, the SPI may include information considered by anindividual user, a group of users, or an organization (e.g., a company,a government or non-government organization, etc.), to be confidentialor proprietary. One example of such confidential information isprotected health information (PHI). As used herein, PHI broadly refersto any information associated with the health status, provision ofhealth care, or payment for health care that is created or collected bya “covered entity,” or an associate thereof, that can be linked to aparticular individual. As used herein, a “covered entity” broadly refersto health plans, healthcare clearinghouses, healthcare providers, andothers, who may electronically communicate any health-relatedinformation associated with a particular individual. Examples of suchPHI may include any part of a patient's medical record, healthcarerecord, or payment history for medical or healthcare services.

As used herein, a user behavior factor 610 broadly refers to informationassociated with a user entity's behavior, whether the behavior occurswithin a physical realm or cyberspace. In certain embodiments, userbehavior factors 610 may include the user entity's access rights 612,the user entity's interactions 614, and the date/time/frequency 616 ofwhen the interactions 614 are enacted. In certain embodiments, the userbehavior factors 610 may likewise include the user entity's location618, and the gestures 620 used by the user entity to enact theinteractions 614.

In certain embodiments, the user entity gestures 620 may include keystrokes on a keypad, a cursor movement, a mouse movement or click, afinger swipe, tap, or other hand gesture, an eye movement, or somecombination thereof. In certain embodiments, the user entity gestures620 may likewise include the cadence of the user's keystrokes, themotion, force and duration of a hand or finger gesture, the rapidity anddirection of various eye movements, or some combination thereof. Incertain embodiments, the user entity gestures 620 may include variousaudio or verbal commands performed by the user.

As used herein, user mindset factors 622 broadly refer to informationused to make inferences regarding the mental state of a user entity at aparticular point in time, during the occurrence of an event or anenactment of a user behavior, or a combination thereof. As likewise usedherein, mental state broadly refers to a hypothetical statecorresponding to the way a user entity may be thinking or feeling.Likewise, as used herein, an event broadly refers to the occurrence ofan action performed by an entity. In certain embodiments, the userentity mindset factors 622 may include a personality type 624. Examplesof known approaches for determining a personality type 624 includeJungian types, Myers-Briggs type indicators, Keirsey Temperament Sorter,Socionics, Enneagram of Personality, and Eyseneck's three-factor model.

In certain embodiments, the user mindset factors 622 may include variousbehavioral biometrics 626. As used herein, a behavioral biometric 628broadly refers to a physiological indication of a user entity's mentalstate. Examples of behavioral biometrics 626 may include a user entity'sblood pressure, heart rate, respiratory rate, eye movements and irisdilation, facial expressions, body language, tone and pitch of voice,speech patterns, and so forth.

Certain embodiments of the invention reflect an appreciation thatcertain user behavior factors 610, such as user entity gestures 620, mayprovide additional information related to inferring a user entity'smental state. As an example, a user entering text at a quick pace with arhythmic cadence may indicate intense focus. Likewise, an individualuser intermittently entering text with forceful keystrokes may indicatethe user is in an agitated state. As another example, the user mayintermittently enter text somewhat languorously, which may indicatebeing in a thoughtful or reflective state of mind. As yet anotherexample, the user may enter text with a light touch with an unevencadence, which may indicate the user is hesitant or unsure of what isbeing entered.

Certain embodiments of the invention likewise reflect an appreciationthat while the user entity gestures 620 may provide certain indicationsof the mental state of a particular user entity, they may not providethe reason for the user entity to be in a particular mental state.Likewise, certain embodiments of the invention include an appreciationthat certain user entity gestures 620 and behavioral biometrics 626 arereflective of an individual user's personality type 624. As an example,aggressive, forceful keystrokes combined with an increased heart ratemay indicate normal behavior for a particular user when composingend-of-month performance reviews. In various embodiments, certain userentity behavior factors 610, such as user gestures 620, may becorrelated with certain contextual information, as described in greaterdetail herein.

In certain embodiments, a security analytics system 118, described ingreater detail herein, may be implemented to include an entity behaviorcatalog (EBC) system 120. In certain embodiments, the EBC system 120 maybe implemented to generate, manage, store, or some combination thereof,information related to the behavior of an associated entity. In variousembodiments, the EBC system 120 may be implemented as a cyber behaviorcatalog. In certain of these embodiments, the cyber behavior catalog maybe implemented to generate, manage, store, or some combination thereof,information related to cyber behavior, described in greater detailherein, enacted by an associated entity. In various embodiments, aslikewise described in greater detail herein, the information generated,managed, stored, or some combination thereof, by such a cyber behaviorcatalog, may be related to cyber behavior enacted by a user entity, anon-user entity, or a combination thereof.

In certain embodiments, the EBC system 120 may be implemented to use auser entity profile 602 in combination with an entity state 636 togenerate a user entity mindset profile 630. As used herein, entity state636 broadly refers to the context of a particular event or entitybehavior. In certain embodiments, the entity state 636 may be along-term entity state or a short-term entity state. As used herein, along-term entity state 636 broadly relates to an entity state 636 thatpersists for an extended interval of time, such as six months or a year.As likewise used herein, a short-term entity state 636 broadly relatesto an entity state 636 that occurs for a brief interval of time, such asa few minutes or a day. In various embodiments, the method by which anentity state's 636 associated interval of time is considered to belong-term or short-term is a matter of design choice.

As an example, a particular user may have a primary work location, suchas a branch office, and a secondary work location, such as theircompany's corporate office. In this example, the user's primary andsecondary offices respectively correspond to the user's location 618,whereas the presence of the user at either office corresponds to anentity state 636. To continue the example, the user may consistentlywork at their primary office Monday through Thursday, but at theircompany's corporate office on Fridays. To further continue the example,the user's presence at their primary work location may be a long-termentity state 636, while their presence at their secondary work locationmay be a short-term entity state 636. Accordingly, a date/time/frequency616 user entity behavior factor 614610 can likewise be associated withuser behavior respectively enacted on those days, regardless of theircorresponding locations. Consequently, the long-term user entity state636 on Monday through Thursday will typically be “working at the branchoffice” and the short-term entity state 636 on Friday will likely be“working at the corporate office.”

As likewise used herein, a user entity mindset profile 630 broadlyrefers to a collection of information that reflects an inferred mentalstate of a user entity at a particular time during the occurrence of anevent or an enactment of a user behavior. As an example, certaininformation may be known about a user entity, such as their name, theirtitle and position, and so forth, all of which are user profileattributes 604. Likewise, it may be possible to observe a user entity'sassociated user behavior factors 610, such as their interactions withvarious systems, when they log-in and log-out, when they are active atthe keyboard, the rhythm of their keystrokes, and which files theytypically use.

Certain embodiments of the invention reflect an appreciation thesebehavior factors 610 can be considered to be a behavioral fingerprint.In certain embodiments, the user behavior factors 610 may change, alittle or a lot, from day to day. These changes may be benign, such aswhen a user entity begins a new project and accesses new data, or theymay indicate something more concerning, such as a user entity who isactively preparing to steal data from their employer. In certainembodiments, the user behavior factors 610 may be implemented toascertain the identity of a user entity. In certain embodiments, theuser behavior factors 610 may be uniquely associated with a particularentity.

In certain embodiments, observed user behaviors may be used to build auser entity profile 602 for a particular user or other entity. Inaddition to creating a model of a user's various attributes and observedbehaviors, these observations can likewise be used to infer things thatare not necessarily explicit. Accordingly, in certain embodiments, abehavioral fingerprint may be used in combination with an EBP 638 togenerate an inference regarding an associated user entity. As anexample, a particular user may be observed eating a meal, which may ormay not indicate the user is hungry. However, if it is also known thatthe user worked at their desk throughout lunchtime and is now eating asnack during a mid-afternoon break, then it can be inferred they areindeed hungry.

As likewise used herein, a non-user entity profile 632 broadly refers toa collection of information that uniquely describes a non-user entity'sidentity and their associated behavior, whether the behavior occurswithin a physical realm or cyberspace. In various embodiments, thenon-user entity profile 632 may be implemented to include certainnon-user profile attributes 634. As used herein, a non-user profileattribute 634 broadly refers to data or metadata that can be used,individually or in combination with other non-user profile attributes634, to ascertain the identity of a non-user entity. In variousembodiments, certain non-user profile attributes 634 may be uniquelyassociated with a particular non-user entity.

In certain embodiments, the non-user profile attributes 634 may beimplemented to include certain identity information, such as a non-userentity's network, Media Access Control (MAC), or physical address, itsserial number, associated configuration information, and so forth. Invarious embodiments, the non-user profile attributes 634 may beimplemented to include non-user behavior information associated withinteractions between certain user and non-user entities, the type ofthose interactions, the data exchanged during the interactions, thedate/time/frequency of such interactions, and certain services accessedor provided.

In certain embodiments, the EBC system 120 may be implemented to includean EBC access management 122, an EBP management 124, a securityvulnerability scenario management 126, a security risk use casemanagement 128, an event enrichment 680, an analytic utility detection682, an entity behavior contextualization 684, an entity behaviormeaning derivation 686, and an entity data anonymization 688 module or acombination thereof. In various embodiments, the EBC access management122 module may be implemented to provide access to certainfunctionalities performed by the EBC system 120, as described in greaterdetail herein. In various embodiments, the EBP management 124 module maybe implemented to perform certain EBP management operations, describedin greater detail herein. In various embodiments, the securityvulnerability scenario management 126 module may be implemented toperform certain security vulnerability scenario management operations,described in greater detail herein.

In various embodiments, the event enrichment 680 module may beimplemented to perform certain event enrichment operations, described ingreater detail herein. In various embodiments, the analytic utilitydetection 682 module may be implemented to perform certain analyticutility detection operations, as described in greater detail herein. Invarious embodiments, as described in greater detail herein, the entitybehavior contextualization 684 module may be implemented to performcertain contextualization operations, as described in greater detailherein. As likewise described in greater detail herein, the entitybehavior meaning derivation 686 module may be implemented in variousembodiments to perform certain behavior meaning derivation operations.In certain embodiments, the entity data anonymization 688 module may beimplemented various embodiments to perform certain data anonymizationoperations. In various embodiments, the EBC access management 122, EBPmanagement 124, security vulnerability scenario management 126, securityrisk use case management 128, event enrichment 680, analytic utilitydetection 682, entity behavior contextualization 684, entity behaviormeaning derivation 686, and entity data anonymization 688 modules, or acombination thereof, may be implemented to provide an EBC referencearchitecture for performing certain EBC operations, described in greaterdetail herein.

In various embodiments, as described in greater detail herein, the EBPsystem 120 may be implemented to use certain data associated with an EBP638 to derive an inference for contextualizing anelectronically-observable behavior of a corresponding entity. In certainembodiments, the EBP system 120 may be implemented to use a user entityprofile 602 in combination with a user entity mindset profile 632 and anassociated entity state 636 to infer a user entity's intent. In certainembodiments, the EBP system 120 may be implemented to use various datastored in a repository of EBP data 690 to perform such an inference. Incertain embodiments, the repository of EBP data 690 may include variousEBPs 638, prepopulated EBPs 678, and associated contextual information,described in greater detail herein.

In various embodiments, the EBC system 120 may be implemented to usecertain data associated with an EBP 638 to provide a probabilisticmeasure of whether a particular electronically-observable event is ofanalytic utility. In certain embodiments, an electronically-observableevent that is of analytic utility may be determined to be anomalous,abnormal, unexpected, or malicious. To continue the prior example, auser may typically work out of their company's corporate office onFridays. Furthermore, various user mindset factors 622 within theirassociated user entity profile 602 may indicate that the user istypically relaxed and methodical when working with customer data.Moreover, the user's user entity profile 602 indicates that such userinteractions 614 with customer data typically occur on Monday morningsand the user rarely, if ever, copies or downloads customer data.However, the user may decide to interact with certain customer data lateat night, on a Friday, while in their company's corporate office. Asthey do so, they exhibit an increased heart rate, rapid breathing, andfurtive keystrokes while downloading a subset of customer data to aflash drive.

Consequently, their user entity mindset profile 630 may reflect anervous, fearful, or guilty mindset, which is inconsistent with theentity state 634 of dealing with customer data in general. Moreparticularly, downloading customer data late at night on a day the useris generally not in their primary office results in an entity state 634that is likewise inconsistent with the user's typical user behavior. Asa result, the EBC system 120 may infer that the user's behavior mayrepresent a security threat. Those of skill in the art will recognizethat many such embodiments and examples are possible. Accordingly, theforegoing is not intended to limit the spirit, scope or intent of theinvention.

Certain embodiments of the invention reflect an appreciation that thequantity, and relevancy, of information contained in a particular EBP638 may have a direct bearing on its analytic utility when attempting todetermine the trustworthiness of an associated entity and whether or notthey represent a security risk. As used herein, the quantity ofinformation contained in a particular EBP 638 broadly refers to thevariety and volume of EBP elements it may contain, and the frequency oftheir respective instances, or occurrences, related to certain aspectsof an associated entity's identity and behavior. As used herein, an EBPelement broadly refers to any data element stored in an EBP 638, asdescribed in greater detail herein. In various embodiments, an EBPelement may be used to describe a particular aspect of an EBP, such ascertain user profile attributes 604, user behavior factors 610, usermindset factors 622, user entity mindset profile 630, non-user profileattributes 634, and entity state 636.

In certain embodiments, statistical analysis may be performed on theinformation contained in a particular EBP 638 to determine thetrustworthiness of its associated entity and whether or not theyrepresent a security risk. For example, a particular authenticationfactor 606, such as a biometric, may be consistently used by a userentity for authenticating their identity to their endpoint device. Tocontinue the example, a user ID and password may be used by the same, ora different user entity, in an attempt to access the endpoint device. Asa result, the use of a user ID and password may indicate a security riskdue to its statistical infrequency. As another example, a user entitymay consistently access three different systems on a daily basis intheir role as a procurement agent. In this example, the three systemsmay include a financial accounting system, a procurement system, and aninventory control system. To continue the example, an attempt by theprocurement agent to access a sales forecast system may appearsuspicious if never attempted before, even if the purpose for accessingthe system is legitimate.

As likewise used herein, the relevancy of information contained in aparticular EBP 638 broadly refers to the pertinence of the EBP elementsit may contain to certain aspects of an associated entity's identity andbehavior. To continue the prior example, an EBP 638 associated with theprocurement agent may contain certain user profile attributes 604related to their title, position, role, and responsibilities, all orwhich may be pertinent to whether or not they have a legitimate need toaccess the sales forecast system. In certain embodiments, the userprofile attributes 604 may be implemented to include certain jobdescription information. To further continue the example, such jobdescription information may have relevance when attempting to determinewhether or not the associated entity's behavior is suspicious. Infurther continuance of the example, job description information relatedto the procurement agent may include their responsibility to check salesforecast data, as needed, to ascertain whether or not to procure certainitems. In these embodiments, the method by which it is determinedwhether the information contained in a particular EBP 638 is ofsufficient quantity and relevancy is a matter of design choice.

Various embodiments of the invention likewise reflect an appreciationthat accumulating sufficient information in an EBP 638 to make such adetermination may take a certain amount of time. Likewise, variousembodiments of the invention reflect an appreciation that theeffectiveness or accuracy of such a determination may rely upon certainentity behaviors occurring with sufficient frequency, or in identifiablepatterns, or a combination thereof, during a particular period of time.As an example, there may not be sufficient occurrences of a particulartype of entity behavior to determine if a new entity behavior isinconsistent with known past occurrences of the same type of entitybehavior.

Various embodiments of the invention reflect an appreciation that asparsely-populated EBP 638 may likewise result in exposure to certainsecurity vulnerabilities. Various embodiments of the invention likewisereflect an appreciation that an EBP 638 may be particularly sparselypopulated when first implemented. Furthermore, the relevance of suchsparsely-populated information initially contained in an EBP 638 firstimplemented may not prove very useful when using an EBP 638 to determinethe trustworthiness of an associated entity and whether or not theyrepresent a security risk. Accordingly, certain embodiments reflect anappreciation that the implementation of a prepopulated EBP 678 mayprovide a sufficient quantity of relevant information to improve theanalytic utility of an EBP 638 when initially implemented. As usedherein, a prepopulated EBP 678 broadly refers to a collection ofinformation that generically describes a particular entity's expectedbehavior, whether the behavior occurs within a physical realm orcyberspace.

In certain embodiments, an entity's expected behavior may be determinedby using one or more existing EBPs 638 associated with similarlysituated entities as a reference when generating a prepopulated EBP 678.As used herein, similarly situated entities broadly refer to entitieswhose associated EBP 638 contain one or more EBP elements sharingsubstantively similar entity characteristics associated with a targetentity. As likewise used herein, entity characteristics broadly refer tocharacteristics that can be used to distinguish certain attributesassociated with a particular entity. Likewise, substantively similarentity characteristics, as used herein, broadly refer to at least oneequivalent entity characteristic, such as the same job title, same jobdescription, same role, one or more of the same duties orresponsibilities, and so forth.

In various embodiments, certain personal information 608, described ingreater detail herein, may be anonymized, as likewise described ingreater detail herein, and used as an entity characteristic. Examples ofsuch anonymized entity characteristics may include name, gender,geographic location, citizenship, country of origin, and so forth. Incertain embodiments, certain user mindset factors 622, such as anentity's personality type 624, may likewise be anonymized and used as anentity characteristic. In certain embodiments, EBPs 638 respectivelyassociated with a collection of distinct entities may be processed todetermine their respective entity characteristics. In certainembodiments, the resulting entity characteristics may be used to segmentthe collection of distinct entities into one or more groups of similarlysituated entities.

In various embodiments, a particular entity characteristic maycorrespond to a user profile attribute, a user behavior factor, or auser mindset factor contained in an EBP 638 associated with one or moresimilarly situated entities. As an example, an organization may employfive financial analysts, each of which has an associated EBP 638containing information related to their observed behavior. In thisexample, the information respectively related to the observed behaviorof the financial analysts may be aggregated and normalized to determine,in general, the expected behavior of a financial analyst.

To continue the example, the resulting information related to theexpected behavior of a financial analyst can then be used as baselinebehavior information for populating a prepopulated EBP 678, which inturn can be associated with a newly-hired financial analyst as their EBP638. It will be appreciated that the implementation of such baselinebehavioral information in certain embodiments may provide a basis forcomparing an entity's expected behavior to their observed behavior, andas a result, assist in the identification of suspicious behavior.

As another example, a security analytics system 118 may be implementedto provide various security services, described in greater detailherein, for a large public school system. In this example, one of theemployees of the school system is their head dietician. One entitycharacteristic of the head dietician is they are a senior administratorin a public school system. Another entity characteristic is they areresponsible for defining cost-effective, nutritional meals for students.Yet another entity characteristic is they are responsible for managing amulti-million dollar budget. Yet still another entity characteristic isthey manage a staff numbering in the hundreds. An additional entitycharacteristic is they are authorized to access the school districtsenterprise resource planning (ERP) system and make adjustments to budgetprojections.

In this example, the first entity characteristic may be used to identifya group of similarly situated entities whose associated EBP 638 signifythey are an administrator in a public school system. Likewise, thesecond entity characteristic may be used to further refine the group ofsimilarly situated entities to identify those entities whose associatedEBP 638 signify they have the role of dietician in a school system, withassociated meal planning responsibilities. In turn, the third entitycharacteristic may likewise be used to yet further refine the group ofsimilarly situated entities to identify those entities whose associatedEBP 638 signify they have a yearly budget responsibility exceeding onemillion dollars.

Additionally, the fourth entity characteristic may be used to yet stillfurther refine the group of similarly situated entities to identifythose entities whose associated EBP 638 signify they manage at least onehundred staff members. Finally, the fifth entity characteristic may beused to yet still further refine the group of similarly situatedentities to identify those entities whose associated EBP 638 signifythey have the right to access systems related to making revisions totheir budget projections. To continue the example, the EBPs 638associated with the resulting group of similarly situated entities maythen be used as the basis to generate a prepopulated EBP 678 for thehead dietician that matches their associated entity characteristics.

Certain embodiments of the invention reflect an appreciation that it maynot always be possible to identify a similarly situated entity who'sassociated EBP 638 signify they have the same entity characteristics asa target entity. Accordingly, the EBPs 638 associated with two or moresimilarly situated entities may be used in certain embodiments togenerate a prepopulated EBP 678 when their respective EBPs 638 signifythey have at least one of the same entity characteristics as the targetentity.

To continue the preceding example, a first similarly situated entity mayhave an associated EBP 638 signifying they are an administrator in apublic school system, they have the role of dietician in a schoolsystem, with associated meal planning responsibilities, and they have ayearly budget responsibility exceeding one million dollars. Likewise, asecond similarly situated entity may have an associated EBP 638signifying they are an administrator in a public school system, theymanage at least one hundred staff members, and they have the right toaccess systems related to making revisions to their budget projections.In continuance of this example the EBPs 638 respectively associated withthe first and second similarly situated entities may be processed togenerate a prepopulated EBP 678 for the head dietician that matchestheir associated entity characteristics.

Certain embodiments of the invention reflect an appreciation that thetwo or more similarly situated entities whose respective EBPs 638 areused to generate a prepopulated EBP 678 may or may not be associated. Infurther continuance of the preceding example, the first and secondsimilarly situated entities may be associated with the same schoolsystem. Conversely, the first and second similarly situated entities maybe associated with different school systems. Skilled practitioners ofthe art will recognize that many such embodiments and examples arepossible. Accordingly, the foregoing is not intended to limit thespirit, scope, or intent of the invention.

In various embodiments, a prepopulated EBP 678 may be implemented toinclude certain parameters describing an entity's expected behavior. Invarious embodiments, certain entity characteristic information, such asjob titles, descriptions, roles, duties, responsibilities, and so forth,may be used to define such parameters in a prepopulated EBP 678. Incertain embodiments, such entity attribute information may be stored ina repository of entity attribute data 690. In various embodiments, aprepopulated EBP 678 may be implemented as an EBP 638 template. Incertain of these embodiments, the EBP 638 template defines whichinformation related to an entity's identity and behavioral will becollected by the EBP 638.

In certain embodiments, the information contained in, or referenced by,a prepopulated EBP 678 may be normalized across multiple entities. Invarious embodiments, certain personally-identifiable information (PH),described in greater detail herein, associated with such entities may beanonymized before its inclusion in a prepopulated EBP 678. In certainembodiments, the anonymization of such PII information may be performedby an entity data anonymization 688 module.

In certain embodiments, a prepopulated EBP 678 may be implemented tomirror the structure of a corresponding EBP 638. For example, as shownin FIG. 6a , an EBP 638 may be implemented to contain a user entityprofile 602, a user entity mindset profile 630, a non-user entityprofile 632, and an entity state 636. As likewise shown in FIG. 6b , acorresponding prepopulated EBP 678 may be implemented to contain aprepopulated user entity profile 642, a prepopulated user entity mindsetprofile 670, a prepopulated non-user entity profile 672, and aprepopulated entity state 676.

As shown in FIG. 6a , the user entity profile 602 of the EBP 638 mayinclude certain user profile attributes 604, user behavior factors 610and user mindset factors 622. Likewise, as shown in FIG. 6a , the userprofile attributes 604 may include certain EBP elements related toauthentication factors 606 and personal information 608. As likewiseshown in FIG. 6a , the user behavior factors 610 may include certain EBPelements related to user access rights 612, user interactions 614,date/time/frequency 616, user location 618, and user gestures 620.Likewise, the user mindset factors 622 shown in FIG. 6a may includecertain EBP elements related to personality type 624 and behavioralbiometrics 626, while the non-user entity profile 632 may includecertain EBP elements related to non-user profile attributes 634.

Likewise, as shown in FIG. 6b , the corresponding prepopulated EBP 678may include certain prepopulated user profile attributes 644,prepopulated user behavior factors 650, and prepopulated user mindsetfactors 662. Likewise, as shown in FIG. 6b , the prepopulated userprofile attributes 604 may include certain prepopulated EBP elementsrelated to prepopulated authentication factors 646 and prepopulatedpersonal information 648. As likewise shown in FIG. 6b , thecorresponding prepopulated user behavior factors 610 may include certainprepopulated EBP elements related to prepopulated user access rights652, prepopulated user interactions 654, prepopulateddate/time/frequency 656, prepopulated user location 658, andprepopulated user gestures 660. Likewise, the prepopulated user mindsetfactors 622 shown in FIG. 6b may include certain prepopulated EBPelements related to prepopulated personality type 664 and prepopulatedbehavioral biometrics 668, while the prepopulated non-user entityprofile 672 may include certain prepopulated EBP elements related toprepopulated non-user profile attributes 674.

As used herein, a prepopulated EBP element broadly refers to any dataelement stored in a prepopulated EBP 678. In certain embodiments, an EBPelement stored in an EBP 638 associated with a particular entity may beused as a prepopulated EBP element in a corresponding prepopulated EBP678. In certain embodiments one or more EBP elements respectively storedin one or more associate EBPs 638 may be used, individually or incombination, as prepopulated EBP elements in a prepopulated EBP 678. Incertain embodiments, the entity data anonymization 688 module may beused to perform anonymization operations to anonymize certain EBPelements prior to being used as prepopulated EBP elements in aprepopulated EBP 678. Skilled practitioners of the art will recognizethat many such embodiments are possible. Accordingly, the foregoing isnot intended to limit the spirit, scope, or intent of the invention.

FIGS. 7a and 7b show a block diagram of a security analytics environmentimplemented in accordance with an embodiment of the invention. Incertain embodiments, a security analytics system 118 may be implementedwith an entity behavior catalog (EBC) system 120, described in greaterdetail herein. In certain embodiments, analyses performed by thesecurity analytics system 118 may be used to identify behaviorassociated with a particular entity that may be of analytic utility. Incertain embodiments, as likewise described in greater detail herein, theEBC system 120 may be used in combination with the security analyticssystem 120 to perform such analyses. In various embodiments, certaindata stored in a repository of security analytics data, or a repositoryof EBC data 690, or both, may be used by the security analytics system118, or the EBC system 120, or both, to perform the analyses.

In certain embodiments, the entity behavior of analytic utility may beidentified at a particular point in time, during the occurrence of anevent, the enactment of a user or non-user entity behavior, or acombination thereof. As used herein, an entity broadly refers tosomething that exists as itself, whether physically or abstractly. Incertain embodiments, an entity may be a user entity, a non-user entity,or a combination thereof. In certain embodiments, a user entity may bean individual user, such as user ‘A’ 702 or ‘B’ 772, a group, anorganization, or a government. In certain embodiments, a non-user entitymay likewise be an item, a device, such as endpoint 304 and edge 202devices, a network, such as an internal 744 and external 746 networks, adomain, an operation, or a process. In certain embodiments, a non-userentity may be a resource 750, such as a geographical location orformation, a physical facility 752, such as a venue, various physicalsecurity devices 754, a system 756, shared devices 758, such as printer,scanner, or copier, a data store 760, or a service 762, such as aservice 762 operating in a cloud environment.

As likewise used herein, an event broadly refers to the occurrence of anaction performed by an entity. In certain embodiments, the action may bedirectly associated with an entity behavior, described in greater detailherein. As an example, a first user may attach a binary file infectedwith a virus to an email that is subsequently sent to a second user. Inthis example, the act of attaching the binary file to the email isdirectly associated with an entity behavior enacted by the first user.In certain embodiments, the action may be indirectly associated with anentity behavior. To continue the example, the recipient of the email mayopen the infected binary file, and as a result, infect their computerwith malware. To further continue the example, the act of opening theinfected binary file is directly associated with an entity behaviorenacted by the second user. However, the infection of the emailrecipient's computer by the infected binary file is indirectlyassociated with the described entity behavior enacted by the seconduser.

In various embodiments, certain user authentication factors 606 may beused to authenticate the identity of a user entity. In certainembodiments, the user authentication factors 606 may be used to ensurethat a particular user entity, such as user ‘A’ 702 or ‘B’ 772, isassociated with their corresponding user entity profile 602, rather thana user entity profile 602 associated with another user. In certainembodiments, the user authentication factors 606 may include a user'sbiometrics 706 (e.g., a fingerprint or retinal scan), tokens 708 (e.g.,a dongle containing cryptographic keys), user identifiers and passwords(ID/PW) 710, and personal identification numbers (PINs).

In certain embodiments, information associated with such user entitybehavior may be stored in a user entity profile 602, described ingreater detail herein. In certain embodiments, the user entity profile602 may be stored in a repository of entity behavior catalog (EBC) data690. In certain embodiments, as likewise described in greater detailherein, the user entity profile 602 may include user profile attributes604, user behavior factors 610, user mindset factors 622, or acombination thereof. As used herein, a user profile attribute 604broadly refers to data or metadata that can be used, individually or incombination with other user profile attributes 604, user behaviorfactors 610, or user mindset factors 622, to ascertain the identity of auser entity. In various embodiments, certain user profile attributes 604may be uniquely associated with a particular user entity.

As likewise used herein, a user behavior factor 610 broadly refers toinformation associated with a user's behavior, whether the behavioroccurs within a physical realm or cyberspace. In certain embodiments,the user behavior factors 610 may include the user's access rights 612,the user's interactions 614, and the date/time/frequency 616 of thoseinteractions 614. In certain embodiments, the user behavior factors 610may likewise include the user's location 618 when the interactions 614are enacted, and the user gestures 620 used to enact the interactions614.

In various embodiments, certain date/time/frequency 616 user behaviorfactors 610 may be implemented as ontological or societal time, or acombination thereof. As used herein, ontological time broadly refers tohow one instant in time relates to another in a chronological sense. Asan example, a first user behavior enacted at 12:00 noon on May 17, 2017may occur prior to a second user behavior enacted at 6:39 PM on May 18,2018. Skilled practitioners of the art will recognize one value ofontological time is to determine the order in which various userbehaviors have been enacted.

As likewise used herein, societal time broadly refers to the correlationof certain user profile attributes 604, user behavior factors 610, usermindset factors 622, or a combination thereof, to one or more instantsin time. As an example, user ‘A’ 702 may access a particular system 756to download a customer list at 3:47 PM on Nov. 3, 2017. Analysis oftheir user behavior profile indicates that it is not unusual for user‘A’ 702 to download the customer list on a weekly basis. However,examination of their user behavior profile also indicates that user ‘A’702 forwarded the downloaded customer list in an email message to user‘B’ 772 at 3:49 PM that same day. Furthermore, there is no record intheir user behavior profile that user ‘A’ 702 has ever communicated withuser ‘B’ 772 in the past. Moreover, it may be determined that user ‘B’872 is employed by a competitor. Accordingly, the correlation of user‘A’ 702 downloading the customer list at one point in time, and thenforwarding the customer list to user ‘B’ 772 at a second point in timeshortly thereafter, is an example of societal time.

In a variation of the prior example, user ‘A’ 702 may download thecustomer list at 3:47 PM on Nov. 3, 2017. However, instead ofimmediately forwarding the customer list to user ‘B’ 772, user ‘A’ 702leaves for a two week vacation. Upon their return, they forward thepreviously-downloaded customer list to user ‘B’ 772 at 9:14 AM on Nov.20, 2017. From an ontological time perspective, it has been two weekssince user ‘A’ 702 accessed the system 756 to download the customerlist. However, from a societal time perspective, they have stillforwarded the customer list to user ‘B’ 772, despite two weeks havingelapsed since the customer list was originally downloaded.

Accordingly, the correlation of user ‘A’ 702 downloading the customerlist at one point in time, and then forwarding the customer list to user‘B’ 772 at a much later point in time, is another example of societaltime. More particularly, it may be inferred that the intent of user ‘A’702 did not change during the two weeks they were on vacation.Furthermore, user ‘A’ 702 may have attempted to mask an intendedmalicious act by letting some period of time elapse between the timethey originally downloaded the customer list and when they eventuallyforwarded it to user ‘B’ 772. From the foregoing, those of skill in theart will recognize that the use of societal time may be advantageous indetermining whether a particular entity behavior is of analytic utility.As used herein, mindset factors 622 broadly refer to information used toinfer the mental state of a user at a particular point in time, duringthe occurrence of an event, an enactment of a user behavior, orcombination thereof.

In certain embodiments, the security analytics system 118 may beimplemented to process certain entity attribute information, describedin greater detail herein, associated with providing resolution of theidentity of an entity at a particular point in time. In variousembodiments, the security analytics system 118 may be implemented to usecertain entity identifier information, likewise described in greaterdetail herein, to ascertain the identity of an associated entity at aparticular point in time. In various embodiments, the entity identifierinformation may include certain temporal information, described ingreater detail herein. In certain embodiments, the temporal informationmay be associated with an event associated with a particular point intime.

In certain embodiments, the security analytics system 118 may beimplemented to use information associated with certain entity behaviorelements to resolve the identity of an entity at a particular point intime. An entity behavior element, as used herein, broadly refers to adiscrete element of an entity's behavior during the performance of aparticular operation in a physical realm, cyberspace, or a combinationthereof. In certain embodiments, such entity behavior elements may beassociated with a user/device 730, a user/network 742, a user/resource748, a user/user 770 interaction, or a combination thereof.

As an example, user ‘A’ 702 may use an endpoint device 304 to browse aparticular web page on a news site on an external system 776. In thisexample, the individual actions performed by user ‘A’ 702 to access theweb page are entity behavior elements that constitute an entitybehavior, described in greater detail herein. As another example, user‘A’ 702 may use an endpoint device 304 to download a data file from aparticular system 756. In this example, the individual actions performedby user ‘A’ 702 to download the data file, including the use of one ormore user authentication factors 606 for user authentication, are entitybehavior elements that constitute an entity behavior. In certainembodiments, the user/device 730 interactions may include an interactionbetween a user, such as user ‘A’ 702 or ‘B’ 772, and an endpoint device304.

In certain embodiments, the user/device 730 interaction may includeinteraction with an endpoint device 304 that is not connected to anetwork at the time the interaction occurs. As an example, user ‘A’ 702or ‘B’ 772 may interact with an endpoint device 304 that is offline,using applications 732, accessing data 734, or a combination thereof, itmay contain. Those user/device 730 interactions, or their result, may bestored on the endpoint device 304 and then be accessed or retrieved at alater time once the endpoint device 304 is connected to the internal 744or external 746 networks. In certain embodiments, an endpoint agent 306may be implemented to store the user/device 730 interactions when theuser device 304 is offline.

In certain embodiments, an endpoint device 304 may be implemented with adevice camera 728. In certain embodiments, the device camera 728 may beintegrated into the endpoint device 304. In certain embodiments, thedevice camera 728 may be implemented as a separate device configured tointeroperate with the endpoint device 304. As an example, a webcamfamiliar to those of skill in the art may be implemented receive andcommunicate various image and audio signals to an endpoint device 304via a Universal Serial Bus (USB) interface.

In certain embodiments, the device camera 728 may be implemented tocapture and provide user/device 730 interaction information to anendpoint agent 306. In various embodiments, the device camera 728 may beimplemented to provide surveillance information related to certainuser/device 730 or user/user 770 interactions. In certain embodiments,the surveillance information may be used by the security analyticssystem 118 to detect entity behavior associated with a user entity, suchas user ‘A’ 702 or user ‘B’ 772 that may be of analytic utility.

In certain embodiments, the endpoint device 304 may be used tocommunicate data through the use of an internal network 744, an externalnetwork 746, or a combination thereof. In certain embodiments, theinternal 744 and the external 746 networks may include a public network,such as the Internet, a physical private network, a virtual privatenetwork (VPN), or any combination thereof. In certain embodiments, theinternal 744 and external 746 networks may likewise include a wirelessnetwork, including a personal area network (PAN), based on technologiessuch as Bluetooth. In various embodiments, the wireless network mayinclude a wireless local area network (WLAN), based on variations of theIEEE 802.11 specification, commonly referred to as WiFi. In certainembodiments, the wireless network may include a wireless wide areanetwork (WWAN) based on an industry standard including various 3G, 4Gand 5G technologies.

In certain embodiments, the user/user 770 interactions may includeinteractions between two or more user entities, such as user ‘A’ 702 and‘B’ 772. In certain embodiments, the user/user interactions 770 may bephysical, such as a face-to-face meeting, via a user/device 730interaction, a user/network 742 interaction, a user/resource 748interaction, or some combination thereof. In certain embodiments, theuser/user 770 interaction may include a face-to-face verbal exchange. Incertain embodiments, the user/user 770 interaction may include a writtenexchange, such as text written on a sheet of paper. In certainembodiments, the user/user 770 interaction may include a face-to-faceexchange of gestures, such as a sign language exchange.

In certain embodiments, temporal event information associated withvarious user/device 730, user/network 742, user/resource 748, oruser/user 770 interactions may be collected and used to providereal-time resolution of the identity of an entity at a particular pointin time. Those of skill in the art will recognize that many suchexamples of user/device 730, user/network 742, user/resource 748, anduser/user 770 interactions are possible. Accordingly, the foregoing isnot intended to limit the spirit, scope or intent of the invention.

In various embodiments, the security analytics system 118 may beimplemented to process certain contextual information in the performanceof certain security analytic operations. As used herein, contextualinformation broadly refers to any information, directly or indirectly,individually or in combination, related to a particular entity behavior.In certain embodiments, entity behavior may include a user entity'sphysical behavior, cyber behavior, or a combination thereof. As likewiseused herein, a user entity's physical behavior broadly refers to anyuser behavior occurring within a physical realm, such as speaking,gesturing, facial patterns or expressions, walking, and so forth. Moreparticularly, such physical behavior may include any action enacted byan entity user that can be objectively observed, or indirectly inferred,within a physical realm. In certain embodiments, the objectiveobservation, or indirect inference, of the physical behavior may beperformed electronically.

As an example, a user may attempt to use an electronic access card toenter a secured building at a certain time. In this example, the use ofthe access card to enter the building is the action and the reading ofthe access card makes the user's physical behaviorelectronically-observable. As another example, a first user mayphysically transfer a document to a second user, which is captured by avideo surveillance system. In this example, the physical transferal ofthe document from the first user to the second user is the action.Likewise, the video record of the transferal makes the first and seconduser's physical behavior electronically-observable. As used herein,electronically-observable user behavior broadly refers to any behaviorexhibited or enacted by a user entity that can be observed through theuse of an electronic device (e.g., an electronic sensor), a computingdevice or system (e.g., an endpoint 304 or edge 202 device, a physicalsecurity device 754, a system 756, a shared device 758, etc.), computerinstructions (e.g., a software application), or a combination thereof.

Cyber behavior, as used herein, broadly refers to any behavior occurringin cyberspace, whether enacted by an individual user, a group of users,or a system acting at the behest of an individual user, a group ofusers, or other entity. More particularly, cyber behavior may includephysical, social, or mental actions that can be objectively observed, orindirectly inferred, within cyberspace. As an example, a user may use anendpoint device 304 to access and browse a particular website on theInternet. In this example, the individual actions performed by the userto access and browse the website constitute a cyber behavior. As anotherexample, a user may use an endpoint device 304 to download a data filefrom a particular system 756 at a particular point in time. In thisexample, the individual actions performed by the user to download thedata file, and associated temporal information, such as a time-stampassociated with the download, constitute a cyber behavior. In theseexamples, the actions are enacted within cyberspace, in combination withassociated temporal information, which makes themelectronically-observable.

In certain embodiments, the contextual information may include locationdata 736. In certain embodiments, the endpoint device 304 may beconfigured to receive such location data 736, which is used as a datasource for determining the user's location 618. In certain embodiments,the location data 736 may include Global Positioning System (GPS) dataprovided by a GPS satellite 738. In certain embodiments, the locationdata 736 may include location data 736 provided by a wireless network,such as from a cellular network tower 740. In certain embodiments (notshown), the location data 736 may include various Internet Protocol (IP)or other network address information assigned to the endpoint 304 oredge 202 device. In certain embodiments (also not shown), the locationdata 736 may include recognizable structures or physical addresseswithin a digital image or video recording.

In certain embodiments, the endpoint devices 304 may include an inputdevice (not shown), such as a keypad, magnetic card reader, tokeninterface, biometric sensor, and so forth. In certain embodiments, suchendpoint devices 304 may be directly, or indirectly, connected to aparticular facility 752, physical security device 754, system 756, orshared device 758. As an example, the endpoint device 304 may bedirectly connected to an ingress/egress system, such as an electroniclock on a door or an access gate of a parking garage. As anotherexample, the endpoint device 304 may be indirectly connected to aphysical security device 754 through a dedicated security network.

In certain embodiments, the security analytics system 118 may beimplemented to perform various risk-adaptive protection operations.Risk-adaptive, as used herein, broadly refers to adaptively respondingto risks associated with an electronically-observable entity behavior.In various embodiments, the security analytics system 118 may beimplemented to perform certain risk-adaptive protection operations bymonitoring certain entity behaviors, assess the corresponding risk theymay represent, individually or in combination, and respond with anassociated response. In certain embodiments, such responses may be basedupon contextual information, described in greater detail herein,associated with a given entity behavior.

In certain embodiments, various information associated with a userentity profile 602, likewise described in greater detail herein, may beused to perform the risk-adaptive protection operations. In certainembodiments, the user entity profile 602 may include user profileattributes 604, user behavior factors 610, user mindset factors 622, ora combination thereof. In these embodiments, the information associatedwith a user entity profile 602 used to perform the risk-adaptiveprotection operations is a matter of design choice.

In certain embodiments, the security analytics system 118 may beimplemented as a stand-alone system. In certain embodiments, thesecurity analytics system 118 may be implemented as a distributedsystem. In certain embodiment, the security analytics system 118 may beimplemented as a virtual system, such as an instantiation of one or morevirtual machines (VMs). In certain embodiments, the security analyticssystem 118 may be implemented as a security analytics service 764. Incertain embodiments, the security analytics service 764 may beimplemented in a cloud environment familiar to those of skill in theart. In various embodiments, the security analytics system 118 may usedata stored in a repository of security analytics data 880 in theperformance of certain security analytics operations, described ingreater detail herein. Those of skill in the art will recognize thatmany such embodiments are possible. Accordingly, the foregoing is notintended to limit the spirit, scope or intent of the invention.

FIG. 8 is a simplified block diagram showing the mapping of an event toa security vulnerability scenario implemented in accordance with anembodiment of the invention. In certain embodiments, an entity behaviorcatalog (EBC) system 120 may be implemented to identify a securityrelated activity, described in greater detail herein. In certainembodiments, the security related activity may be based upon anobservable, likewise described in greater detail herein. In certainembodiments, the observable may include event information correspondingto electronically-observable behavior enacted by an entity. In certainembodiments, the event information corresponding toelectronically-observable behavior enacted by an entity may be receivedfrom an electronic data source, such as the EBC data sources 810 shownin FIGS. 6a , 8, 16, and 17 b.

In certain embodiments, as likewise described in greater detail herein,the EBC system 120 may be implemented to identify a particular event ofanalytic utility by analyzing an associated security related activity.In certain embodiments, the EBC system 120 may be implemented togenerate entity behavior catalog data based upon an identified event ofanalytic utility associated with a particular security related activity.In various embodiments, the EBC system 120 may be implemented toassociate certain entity behavior data it may generate with apredetermined abstraction level, described in greater detail herein.

In various embodiments, the EBC system 120 may be implemented to usecertain EBC data 690 and an associated abstraction level to generate ahierarchical set of entity behaviors 870, described in greater detailherein. In certain embodiments, the hierarchical set of entity behaviors870 generated by the EBC system 120 may represent an associated securityrisk, likewise described in greater detail herein. Likewise, asdescribed in greater detail herein, the EBC system 120 may beimplemented in certain embodiments to store the hierarchical set ofentity behaviors 870 and associated abstraction level information withina repository of EBC data 690. In certain embodiments, the repository ofEBC data 690 may be implemented to provide an inventory of entitybehaviors for use when performing a security operation, likewisedescribed in greater detail herein.

Referring now to FIG. 8, the EBC system 120 may be implemented invarious embodiments to receive certain event information, described ingreater detail herein, corresponding to an event associated with anentity interaction. As used herein, event information broadly refers toany information directly or indirectly related to an event. As likewiseused herein, an event broadly refers to the occurrence of at least oneaction performed by an entity. In certain embodiments, the at least oneaction performed by an entity may include the enactment of an entitybehavior, described in greater detail herein. In certain embodiments,the entity behavior may include an entity's physical behavior, cyberbehavior, or a combination thereof, as likewise described in greaterdetail herein.

Likewise, as used herein, an entity interaction broadly refers to anaction influenced by another action enacted by an entity. As an example,a first user entity may perform an action, such as sending a textmessage to a second user entity, who in turn replies with a response. Inthis example, the second user entity's action of responding isinfluenced by the first user entity's action of sending the textmessage. In certain embodiments, an entity interaction may include theoccurrence of at least one event enacted by one entity when interactingwith another, as described in greater detail herein. In certainembodiments, an event associated with an entity interaction may includeat least one entity attribute, described in greater detail herein, andat least one entity behavior, likewise described in greater detailherein.

In certain embodiments, an entity attribute and an entity behavior maybe respectively abstracted to an entity attribute 872 and an entitybehavior 874 abstraction level. In certain embodiments, an entityattribute 872 and an entity behavior 874 abstraction level may then beassociated with an event 876 abstraction level. In certain embodiments,the entity attribute 872, entity behavior 874, and event 876 abstractionlevels may in turn be associated with a corresponding entity behaviorhierarchy 870, as described in greater detail herein.

In various embodiments, the event information may be received fromcertain EBC data sources 810, such as a user 802 entity, an endpoint 804non-user entity, a network 806 non-user entity, or a system 808 non-userentity. In certain embodiments, one or more events may be associatedwith a particular entity interaction. As an example, as shown in FIG. 8,one or more events i+n 812 may be associated with a user/device 730interaction between a user 802 entity and an endpoint 904 non-userentity. Likewise, one or more events j+n 814 may be associated with auser/network 742 interaction between a user 802 entity and a network 806non-user entity. As likewise shown in FIG. 8, one or more events k+n 916816 may be associated with a user/resource 748 interaction between auser 802 entity and a system 808 non-user entity.

In certain embodiments, details of an event, such as events i+n 812, j+n814, and k+n 816, may be included in their associated event information.In various embodiments, as described in greater detail herein, analyticutility detection operations may be performed on such event informationto identify events of analytic utility. In various embodiments, certainevent information associated with an event determined to be of analyticutility may be used to derive a corresponding observable. As usedherein, an observable broadly refers to an event of analytic utilitywhose associated event information may include entity behavior that maybe anomalous, abnormal, unexpected, malicious, or some combinationthereof, as described in greater detail herein.

As an example, the details contained in the event informationrespectively corresponding to events i+n 812, j+n 814, and k+n 816 maybe used to derive observables i+n 822, j+n 824, and k+n 826. In certainembodiments, the resulting observables i+n 822, j+n 824, and k+n 826 maythen be respectively associated with a corresponding observable 878abstraction level. In certain embodiments, the observable 878abstraction level may in turn be associated with a corresponding entitybehavior hierarchy 870, as described in greater detail herein.

In certain embodiments, the resulting observables may in turn beprocessed to generate an associated security related activity. As usedherein, a security related activity broadly refers to an abstracteddescription of an interaction between two entities, described in greaterdetail herein, which may represent anomalous, abnormal, unexpected,malicious entity behavior. For example, observables i+n 822, j+n 824,and k+n 826 may in turn be processed to generate corresponding securityrelated activities i 832, j 834, and k 836. In certain embodiments, theresulting security related activities, i 832, j 834, and k 836 may thenbe respectively associated with a corresponding security relatedactivity 880 abstraction level. In certain embodiments, the securityrelated activity 880 abstraction level may in turn be associated with acorresponding entity behavior hierarchy 870, as described in greaterdetail herein.

In various embodiments, sessionization and fingerprint generationoperations 820, described in greater detail herein, may be performed toassociate certain events, observables, and security related activities,or a combination thereof, with a corresponding session, likewisedescribed in greater detail herein. As an example, events i+n 812, j+n814, k+n 816, observables i+n 822, j+n 824, k+n 826, and securityrelated activities i 832, j 834, k 836 may be associated withcorresponding sessions. In certain embodiments, a security relatedactivity may be processed with associated contextual information,described in greater detail herein, to generate a corresponding EBPelement.

For example, security related activities i 832, j 834, and k 836 may beprocessed with associated contextual information to generatecorresponding EBP elements i 842, j 844, and k 846. In variousembodiments, the resulting EBP elements i 842, j 844, and k 846 may thenbe associated with a corresponding EBP element 882 abstraction level. Incertain embodiments, the EBP element 882 abstraction level may in turnbe associated with a corresponding entity behavior hierarchy 870, asdescribed in greater detail herein.

In certain embodiments, EBP generation and modification 840 operationsmay be performed to associate one or more EBP elements with a particularEBP 638. As an example, EBP elements i 842, j 844, and k 946 may beassociated with a particular EBP 638, which may likewise be respectivelyassociated with the various entities involved in the user/device 730,user/network 742, or user/resource 748 interactions. In theseembodiments, the method by which the resulting EBP elements i 842, j844, and k 846 are associated with a particular EBP 638 is a matter ofdesign choice. In certain embodiments, the EBP 638 may likewiseassociated with an EBP 884 abstraction level. In certain embodiments,the EBP 884 abstraction level may in turn be associated with acorresponding entity behavior hierarchy 870, as described in greaterdetail herein.

In various embodiments, the resulting EBP 638 may be used in theperformance of security risk use case association 850 operations toidentify one or more security risk use cases that match certain entitybehavior information stored in the EBP 638. As used herein, a securityrisk use case broadly refers to a set of security related activitiesthat create a security risk narrative that can be used to adaptivelydraw inferences, described in greater detail herein, from entitybehavior enacted by a particular entity. In certain of theseembodiments, the entity behavior information may be stored within theEBP 638 in the form of an EBP element, a security related activity, anobservable, or an event, or a combination thereof. In certainembodiments, the security risk use case association operations may beperformed by the security risk use case management 128 module of the EBCsystem 120 described in the text associated with FIG. 6a . In certainembodiments, identified security risk use cases may then be associatedwith a security risk use case 886 abstraction level. In certainembodiments, the security risk use case 886 abstraction level may inturn be associated with a corresponding entity behavior hierarchy 870,as described in greater detail herein.

In certain embodiments, the results of the security risk use caseassociation 850 operations may in turn be used to perform securityvulnerability scenario inference 860 operations to associate one or moresecurity risk use cases with one or more security vulnerabilityscenarios. As used herein, a security vulnerability scenario broadlyrefers to a grouping of one or more security risk use cases thatrepresent a particular class of security vulnerability. In certainembodiments, the security vulnerability scenario association operationsmay be performed by the security vulnerability scenario management 126module of the EBC system 120 described in the text associated with FIG.6a . In certain embodiments, the associated security vulnerabilityscenarios may then be associated with a security vulnerability scenario888 abstraction level. In certain embodiments, the securityvulnerability scenario 888 abstraction level may in turn be associatedwith a corresponding entity behavior hierarchy 870, as described ingreater detail herein.

In various embodiments, certain event information associated with eventsi+n 812, j+n 814, and k+n 816 and certain observable informationassociated with observables i+n 822, j+n 824, and k+n 826 may be storedin a repository of EBC data 690. In various embodiments, certainsecurity related activity information associated with security relatedactivities i 832, j 834, and k 836 and EBP elements i 842, j 844, and k846 may likewise be stored in the repository of EBC data 690. Likewise,in various embodiments, certain security risk use case association andsecurity vulnerability scenario association information respectivelyassociated with the performance of security risk use case association850 and security vulnerability scenario inference 860 operations may bestored in the repository of EBC data 690.

FIG. 9 is a simplified block diagram of the generation of a session anda corresponding session-based fingerprint implemented in accordance withan embodiment of the invention. In certain embodiments, an observable906 may be derived from an associated event, as described in greaterdetail herein. In certain embodiments, one or more observables 906 maybe processed to generate a corresponding security related activity 908.In certain embodiments, one or more security related activities 908 maythen be respectively processed to generate a corresponding activitysession 910. In turn, the session 910 may be processed in certainembodiments to generate a corresponding session fingerprint 912. Incertain embodiments, the resulting activity session 910 and itscorresponding session fingerprint 912, individually or in combination,may then be associated with a particular entity behavior profile (EBP)element 980. In certain embodiments the EBP element 980 may in turn beassociated with an EBP 638.

In certain embodiments, intervals in time 904 respectively associatedwith various security related activities 908 may be contiguous. Forexample, as shown in FIG. 9, the intervals in time 904 associated withobservables 906 ‘1’ 914 and ‘2’ 916 may be contiguous. Accordingly, theintervals in time 904 associated with the security related activities908 ‘1’ 918 and ‘2’ 920 respectively generated from observables 906 ‘1’914 and ‘2’ 916 would likewise be contiguous.

As likewise shown in FIG. 9, the resulting security related activities908 ‘1’ 918 and ‘2’ 920 may be processed to generate an associatedactivity session ‘A’ 922, which then may be processed to generate acorresponding session fingerprint ‘A’ 924. In certain embodiments,activity session ‘A’ 922 and its corresponding session fingerprint ‘A’924 may be used to generate a new entity behavior profile (EBP) element980 ‘A’ 926. In certain embodiments, EBP element 980 ‘A’ 926 generatedfrom activity session 910 ‘A’ 922 and its corresponding sessionfingerprint 912 ‘A’ 924 may be associated with an existing EBP 638.

To provide an example, a user may enact various observables 906 ‘1’ 914to update sales forecast files, followed by the enactment of variousobservables 906 ‘2’ 1016 to attach the updated sales forecast files toan email, which is then sent to various co-workers. In this example, theenactment of observables 906 ‘1’ 914 and ‘2’ 916 result in thegeneration of security related activities 908 ‘1’ 918 and ‘2’ 920, whichin turn are used to generate activity session 910 ‘A’ 922. In turn, theresulting activity session 910 ‘A’ 922 is then used to generate itscorresponding session-based fingerprint 912 ‘A’ 924. To continue theexample, activity session 910 ‘A’ 922 is associated with securityrelated activities 908 ‘1’ 918 and ‘2’ 920, whose associated intervalsin time 904 are contiguous, as they are oriented to the updating anddistribution of sales forecast files via email.

Various aspects of the invention reflect an appreciation that a user mayenact certain entity behaviors on a recurring basis. To continue thepreceding example, a user may typically update sales forecast files anddistribute them to various co-workers every morning between 8:00 AM and10:00 AM. Accordingly, the activity session 910 associated with such arecurring activity may result in a substantively similar sessionfingerprint 912 week-by-week. However, a session fingerprint 912 for thesame session 910 may be substantively different should the user happento send an email with an attached sales forecast file to a recipientoutside of their organization. Consequently, a session fingerprint 912that is inconsistent with session fingerprints 912 associated with pastactivity sessions 910 may indicate anomalous, abnormal, unexpected ormalicious behavior.

In certain embodiments, two or more activity sessions 910 may benoncontiguous, but associated. In certain embodiments, an activitysession 910 may be associated with two or more sessions 910. In certainembodiments, an activity session 910 may be a subset of another activitysession 910. As an example, as shown in FIG. 9, the intervals in time904 respectively associated with observables 906 ‘3’ 914 and ‘6’ 932 maybe contiguous. Likewise, the intervals in time 904 associated withobservables 906 ‘4’ 936 and ‘5’ 938 may be contiguous.

Accordingly, the intervals in time 904 associated with the securityrelated activities 908 ‘4’ 936 and ‘5’ 938 respectively generated fromobservables 906 ‘4’ 928 and ‘5’ 930 would likewise be contiguous.However, the intervals in time 904 associated with security relatedactivities 908 ‘4’ 936 and ‘5’ 938 would not be contiguous with theintervals in time respectively associated with security relatedactivities 908 ‘3’ 934 and ‘6’ 940.

As likewise shown in FIG. 9, the resulting security related activities908 ‘3’ 934 and ‘6’ 940 may be respectively processed to generatecorresponding sessions ‘B’ 942 and ‘D’ 946, while security relatedactivities 908 ‘4’ 936 and ‘5’ 938 may be processed to generate activitysession 910 ‘C’ 944. In turn, activity sessions 910 ‘B’ 942, ‘C’ 944,and ‘D’ 946 are then respectively processed to generate correspondingsession-based fingerprints 912 ‘B’ 948, ‘C’ 950 and ‘D’ 952.

Accordingly, the intervals of time 904 respectively associated withactivity sessions 910 ‘B’ 942, ‘C’ 944, and ‘D’ 946, and theircorresponding session fingerprints 912 ‘B’ 948, ‘C’ 950 and ‘D’ 952, arenot contiguous. Furthermore, in this example activity sessions 910 ‘B’942, ‘C’ 944, and ‘D’ 946, and their corresponding session fingerprints912 ‘B’ 948, ‘C’ 950 and ‘D’ 952, are not associated with the EBP 638.Instead, as shown in FIG. 9, activity sessions 910 ‘B’ 942, ‘C’ 944, and‘D’ 946 are processed to generate activity session 910 ‘E’ 954 andsession fingerprints 912 ‘B’ 948, ‘C’ 950 and ‘D’ 952 are processed togenerate session fingerprint 912 ‘E’ 956. In certain embodiments,activity session ‘E’ 954 and its corresponding session fingerprint ‘E’956 may be used to generate a new EBP element 980 ‘E’ 958. In certainembodiments, EBP element 980 ‘E’ 958 generated from activity session 910‘E’ 954 and its corresponding session fingerprint 912 ‘E’ 956 may beassociated with an existing EBP 638.

Accordingly, session 910 ‘E’ 1054 is associated with activity sessions910 ‘B’ 942, ‘C’ 944, and ‘D’ 946. Likewise, sessions 910 ‘B’ 942, ‘C’944, and ‘D’ 946 are subsets of session 910 ‘E’ 954. Consequently, whilethe intervals of time respectively associated with activity sessions 910‘B’ 942, ‘C’ 944, and ‘D’ 946, and their corresponding sessionfingerprints 912 ‘B’ 948, ‘C’ 950 and ‘D’ 952 may not be contiguous,they are associated as they are respectively used to generate session910 ‘E’ 954 and its corresponding session fingerprint 912 ‘E’ 1056.

To provide an example, a user plans to attend a meeting scheduled for10:00 AM at a secure facility owned by their organization to review aproject plan with associates. However, the user wishes to arrive earlyto prepare for the meeting. Accordingly, they arrive at 9:00 AM and usetheir security badge to authenticate themselves and enter the facility.In this example, the enactment of observables 906 ‘3’ 926 may correspondto authenticating themselves with their security badge and gainingaccess to the facility. As before, observables 906 ‘3’ 926 may be usedto generate a corresponding security related activity 908 ‘3’ 934. Inturn, the security related activity 908 ‘3’ 934 may then be used togenerate session 910 ‘B’ 942, which is likewise used in turn to generatea corresponding session fingerprint 912 ‘B’ 948.

The user then proceeds to a conference room reserved for the meetingscheduled for 10:00 AM and uses their time alone to prepare for theupcoming meeting. Then, at 10:00 AM, the scheduled meeting begins,followed by the user downloading the current version of the projectplan, which is then discussed by the user and their associate for a halfhour. At the end of the discussion, the user remains in the conferenceroom and spends the next half hour making revisions to the project plan,after which it is uploaded to a datastore for access by others.

In this example, observables 906 ‘4’ 928 may be associated with the userdownloading and reviewing the project plan and observables 906 ‘5’ 930may be associated with the user making revisions to the project plan andthen uploading the revised project plan to a datastore. Accordingly,behavior elements 906 ‘4’ 928 and ‘5’ 930 may be respectively used togenerate security related activities 908 ‘4’ 936 and ‘5’ 938. In turn,the security related activities 908 ‘4’ 936 and ‘5’ 938 may then be usedto generate session 910 ‘C’ 944, which may likewise be used in turn togenerate its corresponding session-based fingerprint 912 ‘C’ 950.

To continue the example, the user may spend the next half hourdiscussing the revisions to the project plan with a co-worker.Thereafter, the user uses their security badge to exit the facility. Incontinuance of this example, observables 906 ‘6’ 932 may be associatedwith the user using their security badge to leave the secure facility.Accordingly, observables 906 ‘6’ 932 may be used to generate acorresponding security related activity 908 ‘6’ 940, which in turn maybe used to generate a corresponding session 910 ‘D’ 946, which likewisemay be used in turn to generate a corresponding session fingerprint 912‘D’ 952.

In this example, the intervals of time 904 respectively associated withactivity sessions 910 ‘B’ 942, ‘C’ 944, and ‘D’ 946, and theircorresponding session fingerprints 912 ‘B’ 948, ‘C’ 950, and ‘D’ 952,are not contiguous. However they may be considered to be associated astheir corresponding observables 906 ‘3’ 926, ‘4’ 928, ‘5’ 930, and ‘6’932, all have the common attribute of having been enacted within thesecure facility. Furthermore, security related activities 908 ‘4’ 936and ‘5’ 938 may be considered to be associated as their correspondingobservables 906 have the common attribute of being associated with theproject plan.

Accordingly, while the intervals of time 904 respectively associatedwith activity sessions 910 ‘B’ 942, ‘C’ 944, and ‘D’ 946, and theircorresponding session-based fingerprints 912 ‘B’ 948, ‘C’ 950, and ‘D’952, may not be contiguous, they may be considered to be associated.Consequently, sessions 910 ‘B’ 942, ‘C’ 944, and ‘D’ 946 may beconsidered to be a subset of session 910 ‘E’ 954 and session-basedfingerprints 912 ‘B’ 948, ‘C’ 950, and ‘D’ 952 may be considered to be asubset of session-based fingerprint 912 ‘E’ 956.

In certain embodiments, the interval of time 904 corresponding to afirst activity session 910 may overlap an interval of time 904corresponding to a second activity session 910. For example, observables906 ‘7’ 958 and ‘8’ 960 may be respectively processed to generatesecurity related activities 908 ‘7’ 962 and ‘8’ 964. In turn, theresulting security related activities 908 ‘7’ 962 and ‘8’ 964 arerespectively processed to generate corresponding activity sessions 910‘F’ 966 and ‘G’ 968. Sessions The resulting activity sessions 910 ‘F’966 and ‘G’ 968 are then respectively processed to generatecorresponding session-based fingerprints 912 ‘F’ 970 and ‘G’ 972.

However, in this example activity sessions 910 ‘F’ 966 and ‘G’ 968, andtheir corresponding session fingerprints 912 ‘F’ 970 and ‘G’ 972, arenot associated with the EBP 638. Instead, as shown in FIG. 9, activitysessions 910 ‘F’ 966 and ‘G’ 968 are processed to generate activitysession 910 ‘E’ 954 and session fingerprints 912 ‘F’ 970 and ‘G’ 972 areprocessed to generate session fingerprint 912 ‘H’ 976. In certainembodiments, activity session ‘H’ 974 and its corresponding sessionfingerprint ‘H’ 976 may be used to generate a new EBP element 980 ‘H’978. In certain embodiments, EBP element 980 ‘H’ 978 generated fromactivity session 910 ‘E’ 974 and its corresponding session fingerprint912 ‘E’ 976 may be associated with an existing EBP 638.

Accordingly, the time 904 interval associated with activity session 910‘F’ 966 and its corresponding session fingerprint 912 ‘F’ 970 overlapswith the time interval 904 associated with activity session 910 ‘G’ 968and its corresponding session fingerprint 912 ‘G’ 972. As a result,activity sessions 910 ‘F’ 966 and ‘G’ 968 are subsets of activitysession 910 ‘H’ 974. Consequently, while the intervals of timerespectively associated with activity sessions 910 ‘F’ 966 and ‘G’ 968,and their corresponding session fingerprints 912 ‘F’ 970 and ‘G’ 972 mayoverlap, they are associated as they are respectively used to generateactivity session 910 ‘H’ 974 and its corresponding session fingerprint912 ‘H’ 976.

To provide an example, a user may decide to download various images forplacement in an online publication. In this example, observables 906 ‘7’958 may be associated with the user iteratively searching for, anddownloading, the images they wish to use in the online publication.However, the user may not begin placing the images into the onlinepublication until they have selected and downloaded the first few imagesthey wish to use.

To continue the example, observables 906 ‘8’ may be associated with theuser placing the downloaded images in the online publication.Furthermore, the placement of the downloaded images into the onlinepublication may begin a point in time 904 subsequent to when the userbegan to download the images. Moreover, the downloading of the imagesmay end at a point in time 904 sooner than when the user completes theplacement of the images in the online publication.

In continuance of the example, observables 906 ‘7’ 958 and ‘8’ 960 maybe respectively processed to generate security related activities 908‘7’ 962 and ‘8’ 964, whose associated intervals of time 904 overlap oneanother. Accordingly, the intervals in time 904 associated with activitysessions 910 ‘F’ 966 and ‘G’ 968 will likewise overlap one another asthey are respectively generated from security related activities 908 ‘7’962 and ‘8’ 964.

Consequently, while the intervals of time 904 respectively associatedwith activity sessions 910 ‘F’ 966 and ‘G’ 968, and their correspondingsession fingerprints 912 ‘F’ 970 and ‘G’ 972, may overlap, they may beconsidered to be associated as they both relate to the use of images forthe online publication. Accordingly, activity sessions 910 ‘F’ 1066 and‘G’ 968 may be considered to be a subset of activity session 910 ‘H’ 974and session fingerprints 912 ‘F’ 970 and ‘G’ 972 may be considered to bea subset of session fingerprint 912 ‘H’ 976.

FIG. 10 is a generalized flowchart of session fingerprint generationoperations performed in accordance with an embodiment of the invention.In this embodiment, activity session fingerprint generation operationsare begun in step 1002, followed by the selection of an entity in step1004 for associated entity behavior profile (EBP) element generation. Asused herein, an EBP element broadly refers to any data element stored inan EBP, as described in greater detail herein. In various embodiments,an EBP element may be used to describe a particular aspect of an EBP,such as certain entity behaviors enacted by an entity associated withthe EBP. Ongoing monitoring operations are then performed in step 1006to monitor the selected entity's behavior to detect the occurrence of anevent, described in greater detail herein.

A determination is then made in step 1008 whether an event has beendetected. If not, then a determination is made in step 1026 whether tocontinue monitoring the entity's behavior to detect an event. If so,then the process is continued, proceeding with step 1006. Otherwise,session fingerprint generation operations are ended in step 1028.However, if it was determined in step 1008 that an event was detected,then event data associated with the detected event is processed todetermine whether the event is of analytic utility, as described ingreater detail herein.

A determination is then made in step 1012 to determine whether the eventis of analytic utility. If not, then the process is continued,proceeding with 1026. Otherwise, an observable, described in greaterdetail herein, is derived from the event in step 1014. The resultingobservable is then processed with associated observables in step 1016,as likewise described in greater detail herein, to generate a securityrelated activity. As likewise described in greater detail herein, theresulting security related activity is then processed in step 1018 withassociated security related activities to generate an activity session.

In turn, the resulting activity session is then processed in step 1020to generate a corresponding session fingerprint. The resulting sessionfingerprint is then processed with its corresponding activity session instep 1022 to generate an associated EBP element. The resulting EBPelement is then added to an EPB associated with the entity in step 1024and the process is then continued, proceeding with step 1026.

FIG. 11 is simplified block diagram of process flows associated with theoperation of an entity behavior catalog (EBC) system implemented inaccordance with an embodiment of the invention. In certain embodiments,the EBC system 120 may be implemented to define and manage an entitybehavior profile (EBP) 638, as described in greater detail herein. Incertain embodiments, the EBP 638 may be implemented to include a userentity profile 602, a user entity mindset profile 632, a non-user entityprofile 634, and an entity state 636, or a combination thereof, aslikewise described in greater detail herein.

In certain embodiments, the EBC system 120 may be implemented use aparticular user entity profile 602 in combination with a particularentity state 638 to generate an associated user entity mindset profile632, likewise as described in greater detail herein. In certainembodiments, the EBC system 120 may be implemented to use the resultinguser entity mindset profile 632 in combination with its associated userentity profile 602, non-user entity profile 634, and entity state 638,or a combination thereof, to detect entity behavior of analytic utility.In various embodiments, the EBC system 120 may be implemented to performEBP management 1124 operations to process certain entity attribute andentity behavior information, described in greater detail herein,associated with defining and managing an EBP 638. In variousembodiments, the EBC management operations 1124 may be performed by theEBP management 124 module described in the descriptive text associatedwith FIG. 6 a.

As used herein, entity attribute information broadly refers toinformation associated with a particular entity. In various embodiments,the entity attribute information may include certain types of content.In certain embodiments, such content may include text, unstructureddata, structured data, graphical images, photographs, audio recordings,video recordings, biometric information, and so forth. In certainembodiments, the entity attribute information may include metadata. Incertain embodiments, the metadata may include entity attributes, whichin turn may include certain entity identifier types or classifications.

In certain embodiments, the entity attribute information may includeentity identifier information. In various embodiments, the EBC system120 may be implemented to use certain entity identifier information toascertain the identity of an associated entity at a particular point intime. As used herein, entity identifier information broadly refers to aninformation element associated with an entity that can be used toascertain or corroborate the identity of its corresponding entity at aparticular point in time. In certain embodiments, the entity identifierinformation may include user authentication factors, user entity 602 andnon-user entity 634 profile attributes, user and non-user entitybehavior factors, user entity mindset factors, information associatedwith various endpoint and edge devices, networks, and resources, or acombination thereof.

In certain embodiments, the entity identifier information may includetemporal information. As used herein, temporal information broadlyrefers to a measure of time (e.g., a date, timestamp, etc.), a measureof an interval of time (e.g., a minute, hour, day, etc.), or a measureof an interval of time (e.g., two consecutive weekdays days, or betweenJun. 3, 2017 and Mar. 4, 2018, etc.). In certain embodiments, thetemporal information may be associated with an event associated with aparticular point in time. As used herein, such a temporal event broadlyrefers to an occurrence, action or activity enacted by, or associatedwith, an entity at a particular point in time.

Examples of such temporal events include making a phone call, sending atext or an email, using a device, such as an endpoint device, accessinga system, and entering a physical facility. Other examples of temporalevents include uploading, transferring, downloading, modifying, ordeleting data, such as data stored in a datastore, or accessing aservice. Yet other examples of temporal events include interactionsbetween two or more users, interactions between a user and a device,interactions between a user and a network, and interactions between auser and a resource, whether physical or otherwise. Yet still otherexamples of temporal events include a change in name, address, physicallocation, occupation, position, role, marital status, gender,association, affiliation, or assignment.

As likewise used herein, temporal event information broadly refers totemporal information associated with a particular event. In variousembodiments, the temporal event information may include certain types ofcontent. In certain embodiments, such types of content may include text,unstructured data, structured data, graphical images, photographs, audiorecordings, video recordings, and so forth. In certain embodiments, thetemporal event information may include metadata. In various embodiments,the metadata may include temporal event attributes, which in turn mayinclude certain entity identifier types or classifications, described ingreater detail herein.

In certain embodiments, the EBC system 120 may be implemented to useinformation associated with such temporal resolution of an entity'sidentity to assess the risk associated with a particular entity, at aparticular point in time, and respond with a security operation 1128,described in greater detail herein. In certain embodiments, the EBCsystem 120 may be implemented to respond to such assessments in order toreduce operational overhead and improve system efficiency whilemaintaining associated security and integrity. In certain embodiments,the response to such assessments may be performed by a securityadministrator. Accordingly, certain embodiments of the invention may bedirected towards assessing the risk associated with the affirmativeresolution of the identity of an entity at a particular point in time incombination with its behavior and associated contextual information.Consequently, the EBC system 120 may be more oriented in variousembodiments to risk adaptation than to security administration.

Referring now to FIG. 11, in certain embodiments, EBC system 120operations are begun with the receipt of information associated with aninitial event i 1102. In certain embodiments, information associatedwith an initial event i 1102 may include user entity profile 602attributes, user behavior factors, user entity mindset factors, entitystate information, contextual information, all described in greaterdetail herein, or a combination thereof. In various embodiments, certainuser entity profile 602 data, user entity mindset profile 632 data,non-user entity profile 634 data, entity state 636 data, contextualinformation, and temporal information stored in a repository of EBC data690 may be retrieved and then used to perform event enrichment 1108operations to enrich the information associated with event i 1102. Incertain embodiments, event enrichment 1108 operations may be performedby the event enrichment 680 module of the EBC system 120 described inthe text associated with FIG. 6 a.

Analytic utility detection 1112 operations are then performed on theresulting enriched event i 1102 to determine whether it is of analyticutility. If so, then it is derived as an observable 906, described ingreater detail herein. In certain embodiments, event i+1 1104 throughevent i+n 1106, may in turn be received by the EBC system 120 and beenriched 1008. Analytic utility detection 1112 operations are thenperformed on the resulting enriched event i+1 1104 through event i+n1106 to determine whether they are of analytic utility. Observables 906are then derived from those that are. In certain embodiments, theanalytic utility detection 1112 operations may be performed by theanalytic utility detection module 682 of the EBC system 120 described inthe text associated with FIG. 6 a.

In certain embodiments, security related activity abstraction 1114operations may be performed on the resulting observables 906corresponding to events i 1102, i+1 1104, i+n 1106 to generate anassociated security related activity 908, described in greater detailherein. In various embodiments, a security related activity 908 may beexpressed in a Subject Action Object format and associated withobservables 906 resulting from event information provided by variousreceived from certain EBC data sources, likewise described in greaterdetail herein. In certain embodiments, a security related activityabstraction 1114 operation may be performed to abstract away EBC datasource-specific knowledge and details when expressing an entitybehavior. For example, rather than providing the details associated witha “Windows:4624” non-user entity event, its details may be abstracted to“User Login To Device” security related activity 908.

In various embodiments, sessionization and fingerprint 820 operations,described in greater detail herein, may be performed on eventinformation corresponding to events i 1102, i+1 1104, i+n 1106, theircorresponding observables 906, and their associated security relatedactivities 908, or a combination thereof, to generate sessioninformation. In various embodiments, the resulting session informationmay be used to associate certain events i 1102, i+1 1104, i+n 1106, ortheir corresponding observables 906, or their corresponding securityrelated activities 908, or a combination thereof, with a particularsession.

In certain embodiments, as likewise described in greater detail herein,one or more security related activities 908 may in turn be associatedwith a corresponding EBP element. In various embodiments, thepreviously-generated session information may be used to associate theone or more security related activities 908 with a particular EBPelement. In certain embodiments, the one or more security relatedactivities 908 may be associated with its corresponding EBP elementthrough the performance of an EBP management 1124 operation. Likewise,in certain embodiments, one or more EBP elements may in turn beassociated with the EBP 638 through the performance of an EBP management1124 operation. In certain embodiments, the EBP management 1024operations may be performed by the EBP management 124 module of the EBCsystem 120 described in the text associated with FIG. 6 a.

In various embodiments, certain contextualization information stored inthe repository of EBC data 690 may be retrieved and then used to performentity behavior contextualization 1118 operations to provide entitybehavior context, based upon the entity's user entity profile 602, ornon-user entity profile 634, and its associated entity state 638. Incertain embodiments, the entity behavior contextualization 1118operations may be performed by the entity behavior contextualizationmodule 684 of the EBC system 120, described in the text associated withFIG. 6a . In various embodiments, security risk use case association1118 operations may be performed to associate an EBP 638 with aparticular security risk use case. In certain embodiments, the resultsof the previously-performed entity behavior contextualization 1118operations may be used to perform the security risk use case association850 operations. In certain embodiments, the security risk use caseassociation 850 operations may be performed by the security risk usecase management 128 module of the EBC system 120 described in the textassociated with FIG. 6 a.

In various embodiments, security vulnerability scenario inference 860operations may be performed to associate a security risk use case with aparticular security vulnerability scenario, described in greater detailherein. In various embodiments, certain observables 906 derived fromevents of analytical utility may be used to perform the securityvulnerability scenario inference 860 operations. In various embodiments,certain entity behavior contexts resulting from the performance of theentity behavior contextualization 1118 operations may be used to performthe security vulnerability scenario inference 860 operations. In certainembodiments, the security vulnerability scenario inference 860operations may be performed by the security vulnerability scenariomanagement module 126 of the EBC system 120 described in the textassociated with FIG. 6 a.

In certain embodiments, entity behavior meaning derivation 1126operations may be performed on the security vulnerability behaviorscenario selected as a result of the performance of the securityvulnerability scenario inference 860 operations to derive meaning fromthe behavior of the entity. In certain embodiments, the entity behaviormeaning derivation 1126 operations may be performed by analyzing thecontents of the EBP 638 in the context of the security vulnerabilitybehavior scenario selected as a result of the performance of thesecurity vulnerability scenario inference 860 operations. In certainembodiments, the derivation of entity behavior meaning may includeinferring the intent of an entity associated with event i 1102 and eventi+1 1104 through event i+n 1106. In certain embodiments, the entitybehavior meaning derivation 1126 operations may be performed by theentity behavior meaning derivation module 686 of the EBC system 120described in the text associated with FIG. 6.

In various embodiments, performance of the entity behavior meaningderivation 1126 operations may result in the performance of a securityoperation 1128, described in greater detail herein. In certainembodiments, the security operation 1128 may include a cyber kill chain1130 operation, or a risk-adaptive protection 1132 operation, or both.In certain embodiments, the cyber kill chain 1130 operation may beperformed to disrupt the execution of a cyber kill chain, described ingreater detail herein. In certain embodiments, the risk-adaptiveprotection 1132 operation may include adaptively responding with anassociated risk-adaptive response, as described in greater detailherein.

In various embodiments, the security operation 1128 may include certainrisk mitigation operations being performed by a security administrator.As an example, performance of the security operation 1128 may result ina notification being sent to a security administrator alerting them tothe possibility of suspicious behavior. In certain embodiments, thesecurity operation 1128 may include certain risk mitigation operationsbeing automatically performed by a security analytics system or service.As an example, performance of the security operation 1128 may result ina user's access to a particular system being disabled if an attemptedaccess occurs at an unusual time or from an unknown device.

In certain embodiments, meaning derivation information associated withevent i 1102 may be used to update the user entity profile 602 ornon-user entity profile 634 corresponding to the entity associated withevent i 1102. In certain embodiments, the process is iterativelyrepeated, proceeding with meaning derivation information associated withevent i+1 1104 through event i+n 1106. From the foregoing, skilledpractitioners of the art will recognize that a user entity profile 602,or a non-user entity profile 634, or the two in combination, asimplemented in certain embodiments, not only allows the identificationof events associated with a particular entity that may be of analyticutility, but also provides higher-level data that allows for thecontextualization of observed events. Accordingly, by viewing individualsets of events both in context and with a view to how they may be ofanalytic utility, it is possible to achieve a more nuanced andhigher-level comprehension of an entity's intent.

FIG. 12 is a table showing components of an entity behavior profile(EBP) implemented in accordance with an embodiment of the invention. Invarious embodiments, an EBP 638 may be implemented to certain includeentity attributes 1204 behavioral models 1206, and inferences 1208,along with entity state 636. In certain embodiments, an EBP's 638 entitystate 636 may be short-term, or reflect the state of an entity at aparticular point or interval in time. In certain embodiments, an EBP's638 entity state 636 may be long-term, or reflect the state of an entityat recurring points or intervals in time.

In certain embodiments, an EBP's 638 associated entity attributes 1204may be long-lived. As an example, a particular user entity may have aname, an employee ID, an assigned office, and so forth, all of which arefacts rather than insights. In certain embodiments, a particular entitystate 636 may be sufficiently long-termed to be considered an entityattribute 1204. As an example, a first user and a second user may bothhave an entity state 636 of being irritable. However, the first user mayhave a short-term entity state 636 of being irritable on an infrequentbasis, while the second user may have a long-term entity state 636 of beirritable on a recurring basis. In this example, the long-term entitystate 636 of the second user being irritable may be considered to be anentity attribute 1204. In various embodiments, the determination of whatconstitutes an entity state 636 and an entity attribute 1204 is a matterof design choice. In certain embodiments, various knowledgerepresentation approaches may be implemented in combination with anentity behavior catalog (EBC) system to understand the ontologicalinterrelationship of entity attributes 1104 one or more EBP's 638 maycontain. In these embodiments, the method by which certain entityattributes 1204 are selected to be tracked by an EBC system, and themethod by which they are managed within a corresponding EBP 638, is amatter of design choice.

In certain embodiments, the ATP 638 evolves over time as new events andentity behavior is detected. In certain embodiments, an ATP's 638associated behavioral models 1206, and thus the ATP 638 itself mayevolve over time. In certain embodiments, an ATP's 638 behavioral models1206 may be used by an ATP system to provide insight into how unexpecteda set of events may be. As an example, a behavioral model 1206 mayinclude information related to where a particular user entity works,which devices they may use and locations they may login from, who theymay communicate with, and so forth. Certain embodiments of the inventionreflect an appreciation that such behavioral models 1206 can be usefulwhen comparing observed user and non-user entity behaviors to pastobservations in order to determine how unusual a particular entitybehavior may be.

For example, a user may have more than one EBP 638 associated with aparticular channel, which as used herein broadly refers to a mediumcapable of supporting the electronic observation of a user or non-userbehavior, such as a keyboard, a network, a video stream, and so forth.To continue the example, the user may have a particular set of people hesends emails to from his desktop computer, and does so in an orderly andmethodical manner, carefully choosing his words, and writing longer thanaverage messages compared to his peers. Consequently, analysis of suchan email message will likely indicate it was authored by the user andnot someone else.

However, the same user may also send emails from a second channel, whichis his mobile telephone. When using his mobile telephone, the user'semails are typically short, contains typos and emojis, and his writingstyle is primarily limited to simple confirmations or denials.Consequently, analysis of one such email would likely not reveal whetherthe user was the author or not, due to its brevity. Accordingly, the useof the same channel, which in this example is email, demonstrates theuse of different devices will likely generate different behavioralmodels 1206, which in turn could affect the veracity of associatedinferences 1208.

In certain embodiments, a behavioral model 1206 may be implemented as asession-based fingerprint. As used herein, a session-based fingerprintbroadly refers to a unique identifier of an enactor of user or non-userbehavior associated with a session. In certain embodiments, thesession-based fingerprint may be implemented to determine how unexpectedan event may be, based upon an entity's history as it relates to therespective history of their peer entities. In certain embodiments, thesession-based fingerprint may be implemented to determine whether anentity associated with a particular session is truly who they or itclaims to be or if they are being impersonated. In certain embodiments,the session-based fingerprint may be implemented to determine whether aparticular event, or a combination thereof, may be of analytic utility.In certain embodiments, the session-based fingerprint may include a riskscore, be used to generate a risk score, or a combination thereof.

As likewise used herein, a fingerprint, as it relates to a session,broadly refers to a collection of information providing one or moredistinctive, characteristic indicators of the identity of an enactor ofone or more corresponding user or non-user entity behaviors during thesession. In certain embodiments, the collection of information mayinclude one or more user or non-user profile elements. A user ornon-user profile element, as used herein, broadly refers to a collectionof user or non-user entity behavior elements, described in greaterdetail herein.

As used herein, inferences 1208 broadly refer to things that can beinferred about an entity based upon observations. In certain embodimentsthe observations may be based upon electronically-observable behavior,described in greater detail herein. In certain embodiments, the behaviormay be enacted by a user entity, a non-user entity, or a combinationthereof. In certain embodiments, inferences 1108 may be used to provideinsight into a user entity's mindset or affective state.

As an example, an inference 1208 may be made that a user is unhappy intheir job or that they are facing significant personal financialpressures. Likewise, based upon the user's observed behavior, aninference 1208 may be made that they are at a higher risk of beingvictimized by phishing schemes due to a propensity for clicking onrandom or risky website links. In certain embodiments, such inferences1208 may be implemented to generate a predictive quantifier of riskassociated with an entity's behavior.

In certain embodiments, entity state 636, described in greater detailherein, may be implemented such that changes in state can beaccommodated quickly while reducing the overall volatility of aparticular EBP 638. As an example, a user may be traveling byautomobile. Accordingly, the user's location is changing quickly.Consequently, location data is short-lived. As a result, while thelocation of the user may not be updated within their associated EBP 638as it changes, the fact their location is changing may prove to beuseful in terms of interpreting other location-based data from othersessions. To continue the example, knowing the user is in the process ofchanging their location may assist in explaining why the user appears tobe in two physical locations at once.

FIG. 13 is an activities table showing analytic utility actionsoccurring during a session implemented in accordance with an embodimentof the invention. In certain embodiments, an entity behavior catalog(EBC) system, described in greater detail herein, may be implemented tocapture and record various entity actions 1304 enacted by an entityduring a session 1302, likewise described in greater detail herein. Incertain embodiments, the actions, and their associated sessions, may bestored in an entity behavior profile (EBP) corresponding to a particularentity. In various embodiments, the EBC system may be implemented toprocess information stored in an EBP to determine, as described ingreater detail herein, which actions 1304 enacted by a correspondingentity during a particular session 1302 may be of analytic utility 1308.

Certain embodiments of the invention reflect an appreciation thatmultiple sessions 1302, each of which may be respectively associatedwith a corresponding entity, may occur within the same interval of time1306. Certain embodiments of the invention likewise reflect anappreciation that a single action of analytic utility 1308 enacted by anentity occurring during a particular interval of time 1306 may notappear to be suspicious behavior by an associated entity. Likewise,certain embodiments of the invention reflect an appreciation that theoccurrence of multiple actions of analytic utility 1308 enacted by anentity during a particular session 1302 may be an indicator ofsuspicious behavior.

Certain embodiments reflect an appreciation that a particular entity maybe associated with two or more sessions 1302 that occur concurrentlyover a period of time 1306. Certain embodiments of the inventionlikewise reflect an appreciation that a single action of analyticutility 1308 enacted by an entity occurring during a first session 1302may not appear to be suspicious. Conversely, certain embodiments of theinvention reflect an appreciation that multiple actions of analyticutility 1308 during a second session 1302 may be an indicator ofsuspicious behavior.

As an example, a user may log into the same system from two different IPaddresses, one associated with their laptop computer and the other theirmobile phone. In this example, entity actions 1204 1304 enacted by theuser using their laptop computer may be associated with a first session1302 (e.g. session ‘2’), and entity actions 1304 enacted by the userusing their mobile phone may be associated with a second session 12021302 (e.g., session ‘3’). To continue the example, only one action ofanalytic utility 1308 may be associated with the first session 1302,while three actions of analytic utility 1308 may be associated with thesecond session 1302. Accordingly, it may be inferred the preponderanceof actions of analytic utility 1308 enacted by the user during thesecond session 1302 may indicate suspicious behavior being enacted withtheir mobile phone.

FIG. 14 shows a simplified block diagram of the components of a cyberkill chain associated with a security operation performed in accordancewith an embodiment of the invention. As described herein, cyber killchain is similar to the data theft kill chain. In certain embodiments, asecurity analytics system, or an entity behavior catalog (EBC) system,or both, may be implemented to monitor the behavior of a particularentity, as described in greater detail herein. In certain embodiments,such monitoring may include observing an electronically-observable datasource, such as the EBC data sources shown in FIGS. 6a , 8, 16, and 17b.

In certain embodiments, an observable, described in greater detailherein, may be derived from the electronically-observable data source.In certain embodiments, the observable is associated with an event ofanalytic utility, likewise described in greater detail herein. Incertain embodiments, one or more derived observables may then beassociated with a security related activity, as described in greaterdetail herein. In various embodiments, a particular security activitymay be associated with a component of a cyber kill chain.

Skilled practitioners of the art will be familiar with a kill chain,which was originally used as a military concept related to the structureof an attack. In general, the phases of a military kill chain consistedof target identification, force dispatch to target, decision and orderto attack the target, and destruction of the target. Conversely,breaking or disrupting an opponent's kill chain is a method of defenseor preemptive action.

Those of skill in the art will likewise be familiar with a cyber killchain, developed by the Lockheed Martin company of Bethesda, Md., whichis an adaptation of the military kill chain concept that is commonlyused to trace the stages of a cyberattack. In general, such stagestypically consist of, as shown in FIG. 14, data reconnaissance 1402,data access 1404, data collection 1406, data stockpiling 1408, and dataexfiltration 1410. However, the cyber kill chain concept is not limitedto data exfiltration 1410. It can also be implemented to facilitate theanticipation and recognition of insider threats, social engineering,advanced ransomware, and innovative attacks as they evolve.

In certain embodiments, the cyber kill chain may be implemented toanticipate, recognize, and respond to entity behavior of analyticutility that may be determined to be anomalous, abnormal, unexpected,malicious, or some combination thereof, as described in greater detailherein. In certain embodiments, the response to recognition of a killchain may be to perform an associated security operation, described ingreater detail herein. In certain embodiments, the performance of thesecurity operation may result in disrupting or otherwise interferingwith the performance, or execution, of one or more components, steps, orphases of a cyber kill chain by affecting the performance of thesecurity related activity by its associated entity.

In certain embodiments, a cyber kill chain may consist of morecomponents, step, or phases than those shown in FIG. 14. For example, incertain embodiments, the cyber kill chain may likewise includeintrusion, exploitation, privilege escalation, lateral movement,obfuscation/anti-forensics, and denial of service (DoS). In suchembodiments, the data reconnaissance component 1402 may be executed asan observation stage to identify targets, as well as possible tacticsfor the attack. In certain embodiments, the data reconnaissancecomponent 1402 may not be limited to data exfiltration. For example, itmay be related to other anomalous, abnormal, unexpected, maliciousactivity, such as identity theft.

In certain embodiments, the data access 1404 component may not belimited to gaining access to data. In certain embodiments, the dataaccess 1404 component of a cyber kill chain may be executed as anintrusion phase. In such embodiments, the attacker may use what waslearned in execution of the data reconnaissance 1402 component todetermine how to gain access to certain systems, possibly through theuse of malware or exploitation of various security vulnerabilities. Incertain embodiments, a cyber kill chain may likewise include anexploitation component, which may include various actions and efforts todeliver malicious code and exploit vulnerabilities in order to gain abetter foothold with a system, network, or other environment.

In certain embodiments, a cyber kill chain may likewise include aprivilege escalation component, which may include various actions andefforts to escalate the attacker's privileges in order to gain access tomore data and yet more permissions. In various embodiments, a cyber killchain may likewise include a lateral movement component, which mayinclude moving laterally to other systems and accounts to gain greaterleverage. In certain of these embodiments, the leverage may includegaining access to higher-level permissions, additional data, or broaderaccess to other systems.

In certain embodiments, a cyber kill chain may likewise include anobfuscation/anti-forensics component, which may include various actionsand efforts used by the attacker to hide or disguise their activities.Known obfuscation/anti-forensics approaches include laying false trails,compromising data, and clearing logs to confuse or slow down securityforensics teams. In certain embodiments, the data collection 1406 of acyber kill chain may the collection of data with the intent ofeventually being able to exfiltrate it. In certain embodiments,collected data may be accumulated during a data stockpiling 1408component of a cyber kill chain.

In certain embodiments, a cyber kill chain may likewise include a denialof service (DoS) component, which may include various actions andefforts on the part of an attacker to disrupt normal access for usersand systems. In certain embodiments, such disruption may be performed tostop a cyberattack from being detected, monitored, tracked, or blocked.In certain embodiments, the data exfiltration 1410 component of a cyberkill chain may include various actions and efforts to get data out of acompromised system.

In certain embodiments, information associated with the execution of aparticular cyber kill chain may be associated with a correspondingsecurity vulnerability scenario, described in greater detail herein. Incertain embodiments, one of more components of a particular cyber killchain may be associated with one or more corresponding security relateduse cases, likewise described in greater detail herein. In certainembodiments, performance or execution of a component of a cyber killchain may be disrupted by affecting completion of the risk use case.Those of skill in the art will recognize that many such embodiments arepossible. Accordingly, the foregoing is not intended to limit thespirit, scope, or intent of the invention.

FIGS. 15a and 15b are a generalized flowchart of the performance ofentity behavior catalog (EBC) system operations implemented inaccordance with an embodiment of the invention. In this embodiment, EBCsystem 120 operations are begun with ongoing operations being performedby the EBC system in step 1504 to monitor the receipt of eventinformation to detect the occurrence of an event, described in greaterdetail herein.

A determination is then made in step 1506 to determine whether an eventhas been detected. If not, then a determination is made in step 1536 todetermine whether to continue monitoring the receipt of eventinformation. If so, then the process is continued, proceeding with step1504. If not, then a determination is made in step 1540 whether to endEBC system operations. If not, then the process is continued, proceedingwith step 1504. Otherwise EBC system operations are ended in step 1542.

However, if it was determined in step 1506 that an event was detected,then event enrichment operations, described in greater detail herein,are performed on the event in step 1508. Analytic utility detectionoperations are then performed on the resulting enriched event in step1510 to identify entity behavior of analytic utility, as likewisedescribed in greater detail herein. A determination is then made in step1512 to determine whether the enriched event is associated with entitybehavior of analytic utility. If not, then the process is continued,proceeding with step 1540. Otherwise, an observable is derived from theevent in step 1514, as described in greater detail herein.

The resulting observable is then processed with associated observablesin step 1516 to generate a security related activity, likewise describedin greater detail herein. In turn, the resulting security relatedactivity is processed with associated security related activities instep 1518 to generate an activity session, described in greater detail.Thereafter, as described in greater detail herein, the resultingactivity session is processed in step 1520 to generate a correspondingactivity session. In turn, the resulting activity session is processedwith the activity session in step 1522 to generate an EBP element, whichis then added to an associated EBP in step 1524.

Thereafter, in step 1526, certain contextualization information storedin a repository of EBC data may be retrieved and then used in step 1528to perform entity behavior contextualization operations to generateinferences related to the entity's behavior. The EBP is then processedwith resulting entity behavior inferences in step 1530 to associate theEBP with one or more corresponding risk use cases, as described ingreater detail herein. In turn, the one or more risk use cases are thenassociated in step 1532 with one or more corresponding securityvulnerability scenarios, as likewise described in greater detail herein.

Then, in step 1534, entity behavior meaning derivation operations areperformed on the EBP and each security vulnerability behavior scenarioselected in step 1532 to determine whether the entity's behaviorwarrants performance of a security operation. Once that determination ismade, a subsequent determination is made in step 1536 whether to performa security operation. If not, then the process is continued, proceedingwith step 1540. Otherwise, the appropriate security operation, describedin greater detail herein, is performed in step 1538 and the process iscontinued, proceeding with step 1540.

FIG. 16 shows a functional block diagram of the operation of an entitybehavior catalog (EBC) system implemented in accordance with anembodiment of the invention. In various embodiments, certain EBC-relatedinformation, described in greater detail herein, may be provided byvarious EBC data sources 810, likewise described in greater detailherein. In certain embodiments, the EBC data sources 810 may includeendpoint devices 304, edge devices 202, third party sources 1606, andother 1620 data sources. In certain embodiments, the receipt ofEBC-related information provided by third party sources 1606 may befacilitated through the implementation of one or more Apache NiFiconnectors 1608, familiar to skilled practitioners of the art.

In certain embodiments, activity sessionization and session fingerprintgeneration 1620 operations may be performed on the EBC-relatedinformation provided by the EBC data sources 810 to generate discretesessions. As used herein, activity sessionization broadly refers to theact of turning event-based data into activity sessions, described ingreater detail herein. In these embodiments, the method by which certainEBC-related information is selected to be used in the generation of aparticular activity session, and the method by which the activitysession is generated, is a matter of design choice. As likewise usedherein, an activity session broadly refers to an interval of time duringwhich one or more user or non-user behaviors are respectively enacted bya user or non-user entity.

In certain embodiments, the user or non-user behaviors enacted during anactivity session may be respectively associated with one or more events,described in greater detail herein. In certain embodiments, an activitysession may be implemented to determine whether or not user or non-userbehaviors enacted during the session are of analytic utility. As anexample, certain user or non-user behaviors enacted during a particularactivity session may indicate the behaviors were enacted by an impostor.As another example, certain user or non-user behaviors enacted during aparticular activity session may be performed by an authenticated entity,but the behaviors may be unexpected or out of the norm.

In certain embodiments, two or more activity sessions may be contiguous.In certain embodiments, two or more activity sessions may benoncontiguous, but associated. In certain embodiments, an activitysession may be associated with two or more other activity sessions. Incertain embodiments, an activity session may be a subset of anotheractivity session. In certain embodiments, the interval of timecorresponding to a first activity session may overlap an interval oftime corresponding to a second activity session. In certain embodiments,an activity session may be associated with two or more other activitysessions whose associated intervals of time may overlap one another.Skilled practitioners of the art will recognize that many suchembodiments are possible. Accordingly, the foregoing is not intended tolimit the spirit, scope or intent of the invention.

The resulting activity sessions and session fingerprints are theningested 1616, followed by the performance of data enrichment 1614operations familiar to those of skill in the art. In certainembodiments, user identifier information (ID) information provided by auser ID management system 1612 may be used to perform the dataenrichment 1614 operations. In various embodiments, certain contextualinformation related to a particular entity behavior or event may be usedto perform the data enrichment 1614 operations. In various embodiments,certain temporal information, such as timestamp information, related toa particular entity behavior or event may be used to perform the dataenrichment 1614 operations. In certain embodiments, a repository of EBCdata 690 may be implemented to include repositories of entity attributedata 694, entity behavior data 695, and behavioral model data 696. Invarious embodiments, certain information stored in the repository ofentity attribute data 694 may be used to perform the data enrichmentoperations 1614.

In certain embodiments, the resulting enriched sessions may be stored inthe repository of entity behavior data 695. In certain embodiments, theresulting enriched sessions may be provided to a risk services 422module, described in greater detail herein. In certain embodiments, aslikewise described in greater detail herein, the risk services 422module may be implemented to generate inferences, risk models, and riskscores, or a combination thereof. In certain embodiments, the resultinginferences, risk models, and risk scores, or a combination thereof, maythen be stored in the repository of entity behavioral model data 696.

In certain embodiments, the risk services 422 module may be implementedto provide input data associated with the inferences, risk models, andrisk scores it may generate to a security policy service 1628. Incertain embodiments, the security policy service 1628 may be implementedto use the inferences, risk models, and risk scores to generate securitypolicies. In turn, the security policy service 1628 may be implementedin certain embodiments to export 1630 the resulting security policies toendpoint agents or devices 304, edge devices 202, or other securitymechanisms, where they may be used to limit risk, as described ingreater detail herein. In certain embodiments, an EBC access module 122may be implemented to provide administrative access to variouscomponents of the EBC system 120, as shown in FIG. 16. In certainembodiments, the EBC access management module 122 may include a userinterface (UI), or a front-end, or both, familiar to skilledpractitioners of the art.

FIGS. 17a and 17b are a simplified block diagram showing referencearchitecture components of an entity behavior catalog (EBC) systemimplemented in accordance with an embodiment of the invention forperforming certain EBC operations. In various embodiments, the EBCsystem 120 may be implemented to generate, manage, store, or somecombination thereof, information related to the behavior of anassociated entity. In certain embodiments, the EBC system 120 may beimplemented to provide an inventory of entity behaviors for use whenperforming a security operation, described in greater detail herein. Incertain embodiments, the EBC system 120 may be implemented to include anEBC access management 122, an EBP management 124, a securityvulnerability scenario management 126, a security risk use casemanagement 128, an event enrichment 680, an analytic utility detection682, an entity behavior contextualization 684, an entity behaviormeaning derivation 686 module, an entity data anonymization 688 and arepository of EBC data 690, or a combination thereof.

In various embodiments, the EBC access management 122 module may beimplemented to provide access to certain functionalities performed bythe EBC system 120. In various embodiments, the EBC access management122 module may be implemented to perform certain data managementoperations. In certain embodiments, the data management operations mayinclude storing, indexing, and retrieving data stored in the repositoryof EBC catalog data 690. In various embodiments, the data managementoperations may include certain search functionalities familiar toskilled practitioners of the art. In certain embodiments the EBC accessmanagement 122 module may be implemented with a user interface (UI), ora front end, likewise familiar to those of skill in the art, tofacilitate accessing various functionalities performed by the EBC system120. In various embodiments, the EBC access management 122, EBPmanagement 124, security vulnerability scenario management 126, securityrisk use case management 128, event enrichment 680, analytic utilitydetection 682, entity behavior contextualization 684, entity behaviormeaning derivation 686, and entity data anonymization 688 modules, andthe repository of EBC data 690, or a combination thereof, may beimplemented to provide an EBC reference architecture for performingcertain EBC operations, described in greater detail herein.

In certain embodiments, an entity behavior catalog (EBC) system 120 maybe implemented to identify a security related activity, described ingreater detail herein. In certain embodiments, the security relatedactivity may be based upon an observable, likewise described in greaterdetail herein. In certain embodiments, the observable may include eventinformation corresponding to electronically-observable behavior enactedby an entity. In certain embodiments, the event informationcorresponding to electronically-observable behavior enacted by an entitymay be received from an electronic data source, such as the EBC datasources 810 shown in FIGS. 6a , 8, 16, and 17 b.

In certain embodiments, as likewise described in greater detail herein,the EBC system 120 may be implemented to identify a particular event ofanalytic utility by analyzing an observable associated with a particularsecurity related activity. In certain embodiments, the EBC system 120may be implemented to generate entity behavior catalog data based uponan identified event of analytic utility. In certain embodiments, anobservable 906 may be derived, as described in greater detail herein,from an identified event of analytic utility. In various embodiments,the EBC system 120 may be implemented to associate certain entitybehavior data it may generate with a predetermined abstraction level,described in greater detail herein.

In various embodiments, the EBC system 120 may be implemented to usecertain entity behavior catalog data, and an associated abstractionlevel, to generate a hierarchical set of entity behaviors, described ingreater detail herein. In certain embodiments, the hierarchical set ofentity behaviors generated by the EBC system 120 may represent anassociated security risk, likewise described in greater detail herein.Likewise, as described in greater detail herein, the EBC system 120 maybe implemented in certain embodiments to store the hierarchical set ofentity behaviors within a repository of EBC data 690.

In various embodiments, the EBC system 120 may be implemented to receivecertain event information, described in greater detail herein,corresponding to an event associated with an entity interaction,likewise described in greater detail herein. In various embodiments, theevent information may be generated by, received from, or a combinationthereof, certain EBC data sources 810. In certain embodiments, such EBCdata sources 810 may include endpoint devices, edge devices 202,identity and access 1704 systems familiar to those of skill in the art,as well as various software and data security 1706 applications. Invarious embodiments, EBC data sources 810 may likewise include outputfrom certain processes 1708, network 1710 access and traffic logs,domain 1712 registrations and associated entities, certain resources850, described in greater detail herein, event logs 1714 of all kinds,and so forth.

In certain embodiments, EBC system 120 operations are begun with thereceipt of information associated with a particular event. In certainembodiments, information associated with the event may include userentity profile attributes, user behavior factors, user entity mindsetfactors, entity state information, and contextual information, describedin greater detail herein, or a combination thereof. In certainembodiments, the event may be associated with a user/device, auser/network, a user/resource, or a user/user interaction, as describedin greater detail herein. In various embodiments, certain user entityprofile data, user entity mindset profile data, non-user entity profiledata, entity state data, contextual information, and temporalinformation stored in the repository of EBC data 690 may be retrievedand then used to perform event enrichment operations to enrich theinformation associated with the event. In certain embodiments, the eventenrichment operations may be performed by the event enrichment 680module.

In certain embodiments, analytic utility detection operations, describedin greater detail herein, may be performed on the resulting enrichedevent to determine whether it is of analytic utility. In certainembodiments, the analytic utility detection operations may be performedby the analytic utility detection module 682. In various embodiments,certain contextualization information stored in the repository of EBCdata 690 may be retrieved and then used to perform entity behaviorcontextualization operations, likewise described in greater detailherein, to provide context, based upon the entity's user entity profile,or non-user entity profile, and its associated entity state. In certainembodiments, the entity behavior contextualization operations may beperformed by the entity behavior contextualization module 684.

In various embodiments, an observable 906, described in greater detailherein, may be derived from the resulting enriched, contextualizedevent. As shown in FIG. 17b , examples of such observables may includefirewall file download 1718, data loss protection (DLP) download 1720,and various operating system (OS) events 1722, 1726, and 1734. Aslikewise shown in FIG. 17b , other examples of such observables mayinclude cloud access security broker (CASB) events 1724 and 1732,endpoint spawn 1728, insider threat process start 1730, DLP share 1736,and so forth. In certain embodiments, the resulting observables 906 mayin turn be respectively associated with a corresponding observableabstraction level, described in greater detail herein.

In certain embodiments, security related activity abstractionoperations, described in greater detail herein, may be performed on theresulting observables 906 to generate a corresponding security relatedactivity 908. In various embodiments, a security related activity 908may be expressed in a Subject Action Object format and associated withobservables 906 resulting from event information received from certainEBC data sources 810. In certain embodiments, a security relatedactivity abstraction operation, described in greater detail herein, maybe performed to abstract away EBC data source-specific knowledge anddetails when expressing an entity behavior. For example, rather thanproviding the details associated with a “Windows:4624” non-user entityevent, the security related activity 908 is abstracted to a “User LoginTo Device” OS event 1722, 1726, 1734.

As shown in FIG. 17b , examples of security related activities 908 mayinclude “user downloaded document” 1722, “device spawned process” 1744,“user shared folder” 1746, and so forth. To provide other examples, thesecurity related activity 908 “user downloaded document” 1722 may beassociated with observables 906 firewall file download 1718, DLPdownload 1720, OS event 1722, and CASB event 1724. Likewise, thesecurity related activity 908 “device spawned process” 1744, may beassociated with observables 906, OS event 1726, endpoint spawn 1728, andinsider threat process start 1730. The security related activity 908“user shared folder” 1746 may likewise be associated with observables906 CASB event 1732, OS event 1734, and DLP share 1736.

In certain embodiments, security related activities 908 may in turn berespectively associated with a corresponding security related activityabstraction level, described in greater detail herein. In variousembodiments, activity sessionization operations, likewise described ingreater detail herein, may be performed to respectively associatecertain events and security related activities 908 with correspondingactivity sessions, likewise described in greater detail herein.Likewise, as described in greater detail herein, the resulting sessioninformation may be used in various embodiments to associate certainevents of analytic utility, or their corresponding observables 906, ortheir corresponding security related activities 908, or a combinationthereof, with a particular activity session.

In certain embodiments, the resulting security related activities 908may be processed to generate an associated EBP element 980, as describedin greater detail herein. In various embodiments, the EBP element 980may include user entity attribute 1748 information, non-user entityattribute 1750 information, entity behavior 1752 information, and soforth. In certain of these embodiments, the actual information includedin a particular EBP element 980, the method by which it is selected, andthe method by which it is associated with the EBP element 980, is amatter of design choice. In certain embodiments, the generation of aparticular EBP element 980, and its subsequent management, may beperformed by the EBP management 124 module. In certain embodiments, theEBP elements 980 may in turn be respectively associated with acorresponding EBP element abstraction level, described in greater detailherein.

In various embodiments, certain EBP elements 980 may in turn beassociated with a particular EBP 638. In certain embodiments, the EBP638 may be implemented as a class of user 1762 EBPs, an entity-specificuser 1762 EBP, a class of non-user 1766 EBPs, an entity-specificnon-user 1768 EBP, and so forth. In certain embodiments, classes of user1762 and non-user 1766 EBPs may respectively be implemented as aprepopulated EBP, described in greater detail herein.

In certain embodiments, the association of EBP elements 980 with aparticular EBP 638 may be performed by the EBP management 124 module. Invarious embodiments, certain entity data associated with EBP elements980 associated with the classes of user 1762 and non-user 1766 EBPs maybe anonymized. In certain embodiments, the anonymization of the entitydata may be performed by the entity data anonymization 688 module. Incertain embodiments, the EBP 638 may in turn be associated with an EBPabstraction level, described in greater detail herein.

In certain embodiments, security risk use case association operationsmay be performed to associate an EBP 638 with a particular security riskuse case 1770. As shown in FIG. 17a , examples of such security risk usecases 1770 include “data exfiltration” 1772, “data stockpiling” 1774,“compromised insider” 1776, “malicious user” 1778, and so forth. Invarious embodiments, entity behavior of analytic utility resulting fromthe performance of certain analytic utility detection operations may beused identify one or more security risk use cases 1770 associated with aparticular EBP 638. In certain embodiments, identified security risk usecases may in turn be associated with a security risk use caseabstraction level, described in greater detail herein. In certainembodiments, the security risk use case association, and security riskuse case abstraction level association, operations may be performed bythe security risk use case management 128 module.

In certain embodiments, the results of the security risk use caseassociation operations may be used to perform security vulnerabilityscenario association operations to associate one or more security riskuse cases 1770 to one or more security vulnerability scenarios 1780,described in greater detail herein. As shown in FIG. 17a , examples ofsecurity vulnerability scenarios 1780 include “accidental disclosure”1782, “account takeover” 1784, “theft of data” 1786, “sabotage” 1788,“regulatory compliance” 1790, “fraud” 1792, “espionage” 1794, and soforth. To continue the example, the “theft of data” 1786 securityvulnerability scenario may be associated with the “data exfiltration”1772, “data stockpiling” 1774, “compromised insider” 1776, “malicioususer” 1778 security risk use cases 1770. Likewise, the “sabotage” 1788and “fraud” 1792 security vulnerability scenarios may be respectivelyassociated with some other security risk case 1770. In certainembodiments, the associated security vulnerability scenarios may in turnbe associated with a security vulnerability scenario abstraction level,described in greater detail herein. In certain embodiments, the securityvulnerability scenario association, and the security vulnerabilityabstraction level association, operations may be performed by thesecurity vulnerability scenario management 126 module.

FIG. 18 is a simplified block diagram showing the mapping of entitybehaviors to a risk use case scenario implemented in accordance with anembodiment of the invention. In certain embodiments, an entity behaviorcatalog (EBC) system 120 may be implemented, as described in greaterdetail herein, to receive event information from a plurality of EBC datasources 810, which is then processed to determine whether a particularevent is of analytic utility. In certain embodiments, the EBC system 120may be implemented to derive observables 906 from identified events ofanalytic utility, as likewise described in greater detail herein. Incertain embodiments, the EBC system 120 may be implemented, as describedin greater detail herein, to associate related observables 906 with aparticular security related activity 908, which in turn is associatedwith a corresponding security risk use case 1770. In variousembodiments, certain contextual information may be used, as described ingreater detail herein, to determine which security related activities908 may be associated with which security risk use cases 1770.

In certain embodiments, a single 1860 security related activity 908 maybe associated with a particular security risk use case 1770. Forexample, as shown in FIG. 18, event data may be received from aUnix/Linux® event log 1812 and a Windows® directory 1804. In thisexample, certain event data respectively received from the Unix/Linux®event log 1812 and Windows® directory 1804 may be associated with anevent of analytic utility, which results in the derivation ofobservables 906 “File In Log Deleted” 1822 and “Directory Accessed”1824. To continue the example, the resulting observables 906 “File InLog Deleted” 1822 and “Directory Accessed” 1824 may then be associatedwith the security related activity 908 “Event Log Cleared” 1844. Inturn, the security related activity 908 “Event Log Cleared” 1844 may beassociated with security risk use case 1770 “Administrative Evasion”1858.

In certain embodiments, two or more 1864 security related activities 908may be associated with a particular security risk use case 1770. Forexample, as shown in FIG. 18, event data may be received from anoperating system (OS) 1806, an insider threat 1808 detection system, anendpoint 1810 and a firewall 1812. In this example, certain event datarespectively received from the operating system (OS) 1806, an insiderthreat 1808 detection system, an endpoint 1810 and a firewall 1812 maybe associated with an event of analytic utility. Accordingly,observables 906 “Security Event ID” 1826, “New Connection” 1826, may berespectively derived from the event data of analytical utility receivedfrom the endpoint 1810 and the firewall 1812 EBC data sources 810.Likewise, observables 906 “Connection Established” 1830 and “NetworkScan” 1832 may be respectively derived from the event data of analyticalutility received from the OS 1806, the insider threat 1808 detectionsystem, EBC data sources 810.

To continue the example, the resulting observables 906 “Security EventID” 1826, “New Connection” 1826 and “Connection Established” 1830 may beassociated with security related activity 908 “Device Connected To Port”1846. Likewise, observable 906 “Network Scan” 1832 may be associatedwith security related activity 908 “Network Scan” 1848. In turn, thesecurity related activities 908 “Device Connected To Port” 1846 and“Network Scan” 1832 may be associated with security risk use case 1770“Internal Horizontal Scanning” 1862.

In certain embodiments, a complex set 1868 of security relatedactivities 908 may be associated with a particular security risk usecase 1770. For example, as shown in FIG. 18, event data may be receivedfrom an OS 1814, an internal cloud access security broker (CASB) 1816,an external CASB 1818, and an endpoint 1820. In this example, certainevent data respectively received from the OS 1814, the internal cloudaccess security broker (CASB) 1816, the external CASB 1818, and theendpoint 1820 may be associated with an event of analytic utility.

Accordingly, observables 906 “OS Event” 1834, “CASB Event” 1840, and“New Application” 1842 may be respectively derived from the event dataof analytical utility provided by the OS 1814, the external CASB 1818,and the endpoint 1820 EBC data sources 810. Likewise, a first “CASBEvent ID” 1836 observable 906 and a second “CASB Event ID” 1838observable 906 may both be derived from the event data of analyticalutility received from the internal CASB 1816 EBC data source 810.

To continue the example, the “OS Event” 1834, the first “CASB Event ID”1836, and “New Application” 1842 observables 906 may then berespectively associated with security related activities 908 “New USBDevice” 1850, “Private Shareable Link” 1852, and “File TransferApplication” 1856. Likewise, second “CASB Event ID” 1838 observable 906and the “CASB Event” 1840 observable 906 may then be associated withsecurity related activity 908 “Public Shareable Link 1854” 1848. Inturn, the security related activities 908 “New USB Device” 1850,“Private Shareable Link” 1852, “Public Shareable Link 1854” 1848, and“File Transfer Application” 1856 may be associated with security riskuse case 1770 “Data Exfiltration Preparations” 1866.

FIG. 19 is a simplified block diagram of an entity behavior catalog(EBC) system environment implemented in accordance with an embodiment ofthe invention. In certain embodiments, the EBC system environment may beimplemented to detect user or non-user entity behavior of analyticutility and respond to mitigate risk, as described in greater detailherein. In certain embodiments, the EBC system environment may beimplemented to include a security analytics system 118, likewisedescribed in greater detail herein. In certain embodiments, the securityanalytics system 118 may be implemented to include an EBC system 120.

In certain embodiments, the EBC system 120, as described in greaterdetail herein, may be implemented to use entity behavior information togenerate an entity behavior profile (EBP), likewise as described ingreater detail herein. In certain embodiments, the security analyticssystem 118 may be implemented to use one or more session-basedfingerprints to perform security analytics operations to detect suchuser or non-user entity behavior. In certain embodiments, the securityanalytics system 118 may be implemented to monitor entity behaviorassociated with a user entity, such as a user ‘A’ 702 or user ‘B’ 772.In certain embodiments, the user or non-user entity behavior may bemonitored during user/device 730, user/network 742, user/resource 748,and user/user 770 interactions. In certain embodiments, the user/user770 interactions may occur between a first user, such as user ‘A’ 702and user ‘B’ 772.

In certain embodiments, as described in greater detail herein, anendpoint agent 306 may be implemented on an endpoint device 304 toperform user or non-user entity behavior monitoring. In certainembodiments, the user or non-user entity behavior may be monitored bythe endpoint agent 306 during user/device 730 interactions between auser entity, such as user ‘A’ 702, and an endpoint device 304. Incertain embodiments, the user or non-user entity behavior may bemonitored by the endpoint agent 306 during user/network 742 interactionsbetween user ‘A’ 702 and a network, such as an internal 744 or external746 network. In certain embodiments, the user or non-user entitybehavior may be monitored by the endpoint agent 306 during user/resource748 interactions between user ‘A’ 702 and a resource 750, such as afacility, printer, surveillance camera, system, datastore, service, andso forth. In certain embodiments, the monitoring of user or non-userentity behavior by the endpoint agent 306 may include the monitoring ofelectronically-observable actions respectively enacted by a particularuser or non-user entity. In certain embodiments, the endpoint agent 306may be implemented in combination with the security analytics system 118and the EBC system 120 to detect entity behavior of analytic utility andperform a security operation to mitigate risk.

In certain embodiments, the endpoint agent 306 may be implemented toinclude an event analytics 310 module and an EBP feature pack 2008. Incertain embodiments, the EBP feature pack 1908 may be furtherimplemented to include an event data detector 1910 module, an entitybehavior data detector 1912 module, an event and entity behavior datacollection 1914 module, an analytic utility detection 1916 module, anobservable derivation 1918 module, and a security related activityabstraction 1920 module, or a combination thereof. In certainembodiments, the event data detector 1910 module may be implemented todetect event data, described in greater detail herein, resulting fromuser/device 730, user/network 742, user/resource 748, and user/user 770interactions. In various embodiments, the entity behavior detector 2012module may be implemented to detect certain user and non-user entitybehaviors, described in greater detail herein, resulting fromuser/device 730, user/network 742, user/resource 748, and user/user 770interactions.

In various embodiments, the event and entity behavior data collection1914 module may be implemented to collect certain event and entitybehavior data associated with the user/device 730, user/network 742,user/resource 748, and user/user 770 interactions. In certainembodiments, the analytic utility detection 1916 may be implemented todetect entity behavior of analytic utility associated with eventscorresponding to the user/device 730, user/network 742, user/resource748, and user/user 770 interactions. In various embodiments, theobservable derivation 2018 module may be implemented to deriveobservables, described in greater detail herein, associated with eventsof analytical utility corresponding to the user/device 730, user/network742, user/resource 748, and user/user 770 interactions. In variousembodiments, the security related activity abstraction 1918 module maybe implemented to generate a security related activity, likewisedescribed in greater detail herein, from the observables derived by theobservable derivation 1916 module.

In certain embodiments, the endpoint agent 306 may be implemented tocommunicate the event and entity behavior collected by the event andentity behavior data collector 1914 module, the observables derived bythe observable derivation 1916 module, and the security relatedactivities generated by the security related activity abstraction 1918,or a combination thereof, to the security analytics 118 system. Incertain embodiments, the security analytics system 118 may beimplemented to receive the event and entity behavior data, theobservables, and the security related activities provided by theendpoint agent 306. In certain embodiments, the security analyticssystem 118 may be implemented to provide the event and entity behaviordata, the observables, and the security related activities, or acombination thereof, to the security analytics system 118. In turn, incertain embodiments, the security analytics system 118 may beimplemented in certain embodiments to provide the event and entitybehavior data, the observables, and the security related activities, ora combination thereof, to the EBC system 120 for processing.

In certain embodiment, the EBC system 120 may be implemented to includean entity behavior contextualization 684 module, an EBP sessiongenerator 1982 module, an EBP element generator 1984, or a combinationthereof. In certain embodiments, the EBC system 120 may likewise beimplemented to include an EBP management 124, a security risk use casemanagement 128 module, and a security vulnerability scenario management126 module, or a combination thereof. In certain embodiments, the EBPelement generator 1982 module may be implemented to process the eventand entity behavior data, the observables, and the security relatedactivities provided by the endpoint agent 306 to generate EBP elements,described in greater detail herein. In certain embodiments, the EBPsession generator 1984 may be implemented to use the event and entitybehavior data, the observables, and the security related activitiesprovided by the endpoint agent 306, to generate session information. Incertain embodiments, the EBP session generator 1984 may be implementedto use the resulting session information to generate an activitysession, described in greater detail herein.

In various embodiments, the EBP management 124 module may beimplemented, as described in greater detail herein, to perform certainEBP management operations, described in greater detail herein. Aslikewise described in greater detail herein, certain EBP managementoperations may be performed to associate EBP elements generated by theEBP element generator 1982 module with a corresponding EBP. Likewise,certain EBP management operations may be performed to use the sessioninformation generated by the EBP session generator 1984 module toassociate a particular EBP element with a particular EBP. In variousembodiments, the security risk use case management 128 module may beimplemented to perform certain security risk use case associationoperations, as described in greater detail herein. Likewise, asdescribed in greater detail herein, the security vulnerabilitymanagement 126 module may be implemented in various embodiments toperform certain security vulnerability scenario inference operations.

In certain embodiments, the EBC system 120 may be implemented as adistributed system. Accordingly, various embodiments of the inventionreflect an appreciation that certain modules, or associatedfunctionalities, may be implemented either within the EBC system 120itself, the EBP feature pack 1908, an edge device 202, an internal 744or external 746 network, an external system 780, or some combinationthereof. As an example, the functionality provided, and operationsperformed, by the analytic utility detection 1916, observable derivation2018 and security related activity abstraction 1920 modules may beimplemented within the EBC system 120 in certain embodiments. Likewise,the functionality provided, and operations performed, by the entitybehavior contextualization 684, EBP session generator 1982, and EBPelement generator 1984 may be implemented within the EBP feature pack1908. Those of skill in the art will recognize that many suchimplementations are possible. Accordingly, the foregoing is not intendedto limit the spirit, scope, or intent of the invention.

FIG. 20 is a simplified block diagram of an entity behavior catalog(EBC) system implemented in accordance with an embodiment of theinvention to modify an existing, or generate a new, entity behaviorprofile (EBP). Certain aspects of the invention reflect an appreciationthat the block diagram shown in this figure provides additional detailin support of the flowchart steps shown in FIGS. 21a-c and FIGS. 22a-d .In certain embodiments, as described in greater detail herein, asecurity analytics system 118 may be implemented to include an EBCsystem 120. In certain embodiments, the EBC system 120 may beimplemented to include an EBP management 124 module, or an entity dataanonymization 688 module, or both.

In certain embodiments, the EBC system 120 may be implemented to receivea request 2002 to modify an existing, or generate a new, EBP 638. Invarious embodiments, the request 2002 may be received by the EBPmanagement 124 module, which then performs certain EBP managementoperations to generate a modified 2006 EBP or an entity-specific 2008EBP. As used herein, a modified 2006 EBP broadly refers to an existingEBP 638 associated with a particular entity 2004 whose entity behaviorinformation collection parameters have been revised. In certainembodiments, the revisions to the EBP's 638 entity behavior informationcollection parameters may include increasing, decreasing, or otherwisechanging the amount, type, or nature of entity behavior information thatis collected.

As an example, a user entity 2004 may be employed as a claims processorfor an insurance company and have an associated EBP 638 containinginformation related to their identity and behavior in that role. In thisexample, the user entity 2004 may accept a new position as a claimsadjustor, which in turn may involve accessing certain additionalinformation resources. As a result, the user entity's 2004 associatedEBP 638 may be processed by the EBP management 124 module to generate amodified EBP 2006, which in turn is then processed to contain certainEBP elements associated with the new information resources. Once themodified EBP 2006 is generated, it can then be associated with the userentity 2004 as their current EBP 638.

In various embodiments, the EBP's 638 information collection parametersmay be associated with certain EBP elements, described in greater detailherein. In certain of these embodiments, the information collected by,or associated with, a particular EBP 638, whether directly orindirectly, may be related to its associated entity's 2004 identity andentity behavior, as likewise described in greater detail herein. Incertain embodiments, a modified EBP 2006 generated by the EBP management124 module from an existing EBP 638 associated with a particular entity2004 may be implemented to replace the entity's 2004 existing EBP 638.

In certain embodiments, a modified EBP 2006 generated by the EBPmanagement 124 module from an existing EBP 638 associated with aparticular entity 2004 may be implemented to be associated with the sameentity 2004 in addition to its existing EBP 638. As an example, a userentity 2004 may have a primary role in an organization, and as a resulthave a primary EBP 638. However, the same user entity 2004 may beassigned a secondary role. As a result, the user entity's 2004 existingEBP 638 may be processed by the EBP management 124 module to generate amodified EBP 2006, which in turn is revised to accommodate entitybehavior information related to the user entity's 2004 secondary role.The resulting modified EBP 2006 can then be associated with the userentity 2004 as a second EBP 638.

In certain embodiments, a modified EBP 2006 generated by the EBPmanagement 124 module from a particular EBP 638 associated with a firstentity 2004 may be implemented to be associated with a second entity2004. For example, a user entity 2004 may be employed by a company as anaccount executive. As a result, information related to their associatedbehavior may be stored in an associated EBP 638. In this example,another user entity 2004 may be hired as a second account executive. Tocontinue the example, the first user entity's 2004 existing EBP 638 maybe processed by the EBP management 124 module to generate a modified EBP2006, which in turn can then be associated with the second user entity2004 as their initial EBP 638.

In various embodiments, a prepopulated EBP 678 may be implemented tocontain certain prepopulated EBP elements that have been anonymized. Incertain of these embodiments, the entity data anonymization 688 modulemay be implemented to perform the anonymization of the prepopulated EBPelements. In various embodiments, a modified EBP 2006 generated by theEBP management 124 module from a particular EBP 638 associated with afirst entity 2004 may be implemented to contain certain EBP elementsthat have been anonymized prior to being associated with a second entity2004. To continue the prior example, any personally-identifiableinformation (PII) associated with the first user entity 2004 in themodified EBP 2006 is anonymized by the entity data anonymization 688module. Once all PII has been anonymized, the resulting modified EBP2006 can then be associated with the second user entity 2004 as a theirinitial EBP 638.

As likewise used herein, an entity-specific 2008 EBP broadly refers toan EBP 638 that may contain certain EBP elements related to a particularentity 2004, but not yet associated with the entity 2004. For example, auser entity 2004 may join an organization, and as a result, providecertain PII, which in turn may be processed with certain entityattribute data to generate user credentials, authentication factors,access rights and so forth. In this example the resulting EBP elementsmay then be used to populate an entity-specific EBP 2006, which in turnmay then be associated with the user entity 2004 as their initial EBP638. In this example, information related to the user entity's behavioris then added to their EBP 638 over time.

In various embodiments, an entity-specific 2008 EBP implemented as anEBP 638 may be associated with a user entity 2004 whose identity may notbe initially known. As an example, an unknown user entity 2004 may beginnavigating the web site of an organization. In this embodiment, anentity-specific 2008 may be implemented as an EBP 638, which is thenassociated with the unknown user entity 2004. From that point forward,the unknown user entity's 2004 behavior is captured and stored in itsassociated EBP 638.

In certain of these embodiments, the identity of an entity 2004associated with an EBP 638 may be established over time. To continue theprior example, at some point in time, the unknown user entity 2004 maybe requested to provide some form of identity information. If there is apositive response to the request, then the identity information is addedto the EBP 638 associated with the user entity 2004.

In certain embodiments, the EBP management 124 module may be implementedto generate a prepopulated EBP 678, described in greater detail herein.In various embodiments, the EBP generator 124 module may be implementedto process one or more prepopulated EBPs 678 previously-generated by theEBP management 124 module to in turn generate an EBP 638. In certain ofthese embodiments, the resulting EBP 638 may then be associated with oneor more entities 2004. In these embodiments, the method by which theentity 2004 is selected, and the resulting EBP 638 is associated withit, is a matter of design choice.

In certain embodiments, a prepopulated EBP 678 may be implemented as auniversal 2010, a segmented 2012, an entity-pertinent 2014, orentity-specific 2016 prepopulated EBP. As used herein, a universal 2010prepopulated EBP broadly refers to a prepopulated EBP 678 implemented toaccommodate certain prepopulated EBP elements, as described in greaterdetail herein. In certain embodiments, the universal 2010 prepopulatedEBP may be implemented to accommodate prepopulated EBP elementscorresponding to EBP elements supported by a particular EBP 638.

As an example, an EBP 638 may be implemented in certain embodiments toinclude EBP elements associated with a user entity profile, a userentity mindset profile, a non-user entity profile, and an entity state,as described in greater detail herein. Accordingly, a correspondinguniversal 2010 prepopulated EBP may be implemented in certainembodiments to include prepopulated EBP elements associated with aprepopulated user entity profile, a prepopulated user entity mindsetprofile, a prepopulated non-user entity profile, and a prepopulatedentity state, as likewise described in greater detail herein.

In certain embodiments, prepopulated EBP elements associated with aprepopulated user entity profile may include prepopulated user profileattributes, prepopulated user behavior attributes, and prepopulated usermindset factors, as likewise describe in greater detail herein. Invarious embodiments, prepopulated EBP elements associated with aprepopulated non-user entity profile may likewise include certainprepopulated non-user profile attributes, as described in greater detailherein. In these embodiments, the prepopulated EBP elements selected forinclusion in a universal 2010 prepopulated EBP, and the method by whichthey are selected, is a matter of design choice.

As used herein, a segmented 2012 prepopulated EBP broadly refers to aprepopulated EBP 678 implemented to be composed of distinct groups, orsegments, of prepopulated EBP elements. In certain embodiments, one ormore segments of a segmented 2012 prepopulated EBP may be used,individually or in combination, by the EBP management 124 module togenerate an EBP 638. In various embodiments, the prepopulated EBPelements selected for use as segment of a segmented 2012 EBP, and themethod by which they are selected, is a matter of design choice.

As an example, in certain embodiments, the segments of a prepopulatedEBP 678 selected for use as a segmented 2012 prepopulated EBP mayinclude prepopulated EBP elements associated with a prepopulated userentity profile, a prepopulated user entity mindset profile, aprepopulated non-user entity profile, or a prepopulated entity state, ora combination thereof. As another example, in certain embodiments, thesegments of a prepopulated EBP 678 selected for use as a segmented 2012prepopulated EBP may include prepopulated EBP elements associated withprepopulated user profile attributes, prepopulated user behaviorattributes, prepopulated user mindset factors, and prepopulated non-userprofile attributes. Those of skill in the art will recognize many suchembodiments are possible. Accordingly, the foregoing is not intended tolimit the spirit, scope, or intent of the invention.

As used herein, an entity-pertinent 2014 prepopulated EBP broadly refersto a prepopulated EBP 678 that contains one or more prepopulated EBPelements commonly associated with a particular type or class of entity2004. As an example, a cloud service provider may currently have threeservers of the same manufacturer and configuration providing emailservice. Over time, the number of email accounts being serviced may growand the capacity of the three current servers may begin to surpass theiroptimum operational parameters. As a result, the service provider maydecide to purchase and deploy two additional servers from the samemanufacturer with the same configuration to maintain optimum servicelevels.

In this example, all five servers are from the same manufacturer, havethe same configuration, and are used for the same purpose. As a result,they can be considered a class of non-user entities, described ingreater detail herein. To continue the example, the three originalservers may each have an associated EBP 638. In further continuance ofthis example, certain EBP elements contained in the original EBPs 638may be processed by the EBP management 124 module to generate anormalized set of EBP elements. In this example, the resulting set ofnormalized EBP elements can then be used as prepopulated EBP elements,likewise described in greater detail herein, by the EBP management 124module to generate an entity-pertinent 2014 prepopulated EBP. In turn,the resulting entity-pertinent 2014 prepopulated EBP can be associatedwith the two new servers as their respective EBPs 638.

As used herein, an entity-specific 2016 prepopulated EBP broadly refersto a prepopulated EBP 678 that contains one or more prepopulated EBPelements associated with a particular entity 2004. As an example, acompany may have a vice president of sales, a senior sales executive,and an inside sales associate, all of which have an associated EBP 638reflecting their associated user entity's identity, access rights, andbehavior. In this example, the company decides to hire an additionalinside sales associate. Since there is only one other inside salesassociate, their current EBP 638 may be retrieved and processed togenerate an entity-specific 2016 prepopulated EBP. In certainembodiments, the entity data anonymization 688 module may be implementedto anonymize PII in the resulting entity-specific 2016 prepopulated EBP.Once any PII the entity-specific 2016 prepopulated EBP may contain hasbeen anonymized, it can be associated with the newly-hired inside salesassociate as their EBP 638.

In certain embodiments, the EBP management 124 module may be implementedto process a particular modified 2010 EBP for conversion into aprepopulated 678 EBP. In various embodiments, the EBP management 124module may be implemented to use the entity data anonymization 688module when converting a modified EBP 2010 into a prepopulated EBP 678to anonymize certain entity behavior profile 693 entity attribute 694,entity behavior 695, or entity behavior model 696 data associated withan entity 2004. In certain embodiments, the EBP elements contained in aprepopulated EBP 678 may be implemented to contain, or reference,structured data, non-structured data, or a combination thereof.

In various embodiments, prepopulated EBP 678 modification and generationoperations may be initiated by the receipt of a request 2002 to performcertain EBP operations to modify an existing, or generate a new,prepopulated EBP 678. In certain of these embodiments, the request 2002is processed by the EBP management 124 module to retrieve relevant EBP693, entity attribute 694, entity behavior 695, and entity behaviormodel 696 data associated with one or more entities 2004. If the request2002 is to modify an existing prepopulated EBP 678, then the request2002 may be processed in certain embodiments with the retrieved entity692 and EBP 690 data, and the existing prepopulated EBP 678, todetermine which modifications are to be made. If it is decided to useone or more other prepopulated EBPs 678 relevant to making themodifications, then the request 2002, the retrieved EBP 693, entityattribute 694, entity behavior 695, and entity behavior model 696 data,and the existing prepopulated EBP 678, is processed to identify otherrelevant prepopulated EBPs 678 that may be used to make themodifications.

Once identified, the identified prepopulated EBPs 678 are retrieved andthen processed with the request 2002, the retrieved EBP 693, entityattribute 694, entity behavior 695, and entity behavior model 696 data,and the existing prepopulated EBP 678, to generate a modifiedprepopulated EBP 678. In certain embodiments, the modified prepopulatedEBP 678 may be generated as a universal 2010, a segmented 2012, anentity-pertinent 2014, or an entity-specific 2016 prepopulated EBP. Theresulting prepopulated EBP 678 may then be stored in the repository ofEBP data 693.

In certain embodiments, it may be decided to not use one or more otherprepopulated EBPs 678 relevant to making the modifications. If so, thenthe request 2002, the retrieved EBP 693, entity attribute 694, entitybehavior 695, and entity behavior model 696 data, and the existingprepopulated EBP 678 are processed to generate a modified prepopulatedEBP 678 as previously described. As likewise previously described, theresulting prepopulated EBP 678 may then be stored in the repository ofEBP data 693.

In various embodiments, it may be decided to use a universal 2010 EBP togenerate a new prepopulated EBP 678 instead of modifying an existingprepopulated EBP 678. In certain of these embodiments, the request 2002,the retrieved EBP 693, entity attribute 694, entity behavior 695, andentity behavior model 696 data, and a target universal 2010 prepopulatedEBP are then processed to generate a new prepopulated EBP 678. Aspreviously described, the resulting prepopulated EBP 678 may then bestored in the repository of EBP data 693.

In various embodiments, it may be decided to use a segmented 2012 EBP togenerate a new prepopulated EBP 678 instead of modifying an existingprepopulated EBP 678. In certain of the embodiments, the request 2002,along with relevant EBP 693, entity attribute 694, entity behavior 695,and entity behavior model 696 data is processed to identify relevantsegments of one or more target segmented 2012 prepopulated EBPs. Inturn, the request 2002, the relevant EBP 693, entity attribute 694,entity behavior 695, and entity behavior model 696 data, and theidentified relevant EBP segments, are then processed to generate a newprepopulated EBP 678. As previously described, the resultingprepopulated EBP 678 may then be stored in the repository of EBPs 693data.

In various embodiments, it may be decided to use an entity-pertinent2014 EBP to generate a new prepopulated EBP 678 instead of modifying anexisting prepopulated EBP 678. In certain of these embodiments, therequest 2002, the retrieved EBP 693, entity attribute 694, entitybehavior 695, and entity behavior model 696 data, and a targetentity-pertinent 2014 prepopulated EBP, are then processed to generate anew prepopulated EBP 678. As previously described, the resultingprepopulated EBP 678 may then be stored in a repository of EBP 693 data.

In various embodiments, it may be decided to use an entity-specific 2016EBP to generate a new prepopulated EBP 678 instead of modifying anexisting prepopulated EBP 678. In certain of these embodiments, therequest 2002, the retrieved EBP 693, entity attribute 694, entitybehavior 695, and entity behavior model 696 data, and a targetentity-specific 2016 prepopulated EBP, are then processed to generate anew prepopulated EBP 678. As previously described, the resultingprepopulated EBP 678 may then be stored in a repository of EBP 693 data.

In various embodiments, EBP 638 modification and generation operationsmay be initiated by the receipt of a request 2002 to perform certain EBPmanagement operations to modify an existing, or generate a new,prepopulated EBP 638 for a target entity 2004. In certain of theseembodiments, the request 2002 is processed to retrieve relevant EBP 693,entity attribute 694, entity behavior 695, and entity behavior model 696data associated with one or more entities 2004.

If the request 2002 is to modify an existing EBP 638, then the request2002 may be processed in certain embodiments with the retrieved EBP 693,entity attribute 694, entity behavior 695, and entity behavior model 696data, and the existing EBP 638, to determine which modifications are tobe made. If it is decided to use one or more other EBPs 638 relevant tomaking the modifications, then the request 2002, the retrieved EBP 693,entity attribute 694, entity behavior 695, and entity behavior model 696data, and the existing EBP 638, is processed to identify other relevantEBPs 638 that may be used to make the modifications. In certainembodiments, the relevant EBPs 638 that may be used to make themodifications may be stored in the repository of EBP 693 data.

Once identified, the identified other EBPs 638 are retrieved and thenprocessed with the request 2002, the retrieved EBP 693, entity attribute694, entity behavior 695, and entity behavior model 696 data, and theexisting EBP 638, to generate a modified EBP 638. The resulting modifiedEBP 638 may then be associated with the entity 2004. In certainembodiments, it may be decided to not use one or more other EBPs 638relevant to making the modifications. If so, then the request 2002, theretrieved EBP 693, entity attribute 694, entity behavior 695, and entitybehavior model 696 data, and the existing EBP 638, are processed togenerate a modified EBP 2006 as previously described. As previouslydescribed, the resulting modified EBP 2006 may then be associated withthe entity 2004.

In various embodiments, it may be decided to use a universal 2010 EBP togenerate a new EBP 638 instead of modifying an existing EBP 638. Incertain of these embodiments, the request 2002, the retrieved EBP 693,entity attribute 694, entity behavior 695, and entity behavior model 696data, and a target universal 2010 prepopulated EBP, are then processedto generate a new EBP 638. As previously described, the resulting newEBP 638 may then be associated with the entity 2004.

In various embodiments, it may be decided to use a segmented 2012 EBP togenerate a new EBP 638 instead of modifying an existing EBP 638. Incertain embodiments, the request 2002, along with relevant EBP 693,entity attribute 694, entity behavior 695, and entity behavior model 696data is processed to identify relevant segments of one or more targetsegmented 2012 prepopulated EBPs. In turn, the request 2002, therelevant EBP 693, entity attribute 694, entity behavior 695, and entitybehavior model 696 data, and the identified relevant EBP segments, arethen processed to generate a new EBP 638. As previously described, theresulting new EBP 638 may then be associated with the entity 2004.

In various embodiments, it may be decided to use an entity-pertinent2014 EBP to generate a new EBP 638 instead of modifying an existing EBP638. In certain of these embodiments, the request 2002, the retrievedEBP 693, entity attribute 694, entity behavior 695, and entity behaviormodel 696 data, and a target entity-pertinent 2014 prepopulated EBP, arethen processed to generate a new EBP 638. As previously described, theresulting new EBP 638 may then be associated with the entity 2004.

In various embodiments, it may be decided to use an entity-specific 2016prepopulated EBP to generate a new EBP 638 instead of modifying anexisting EBP 638. In certain of these embodiments, the request 2008, theretrieved EBP 693, entity attribute 694, entity behavior 695, and entitybehavior model 696 data, and a target entity-specific 2016 prepopulatedEBP are then processed to generate a new EBP 638. As previouslydescribed, the resulting new EBP 638 may then be associated with theentity 2004.

FIGS. 21a through 21c are a generalized flowchart of entity behaviorprofile (EBP) system operations performed in accordance with anembodiment of the invention to generate a new, or modify an existing,ATP for an associated entity. In this embodiment, EBP modification andgeneration operations are begun in step 2102, followed by the receipt ofa request in step 2104 to perform certain EBP operations to modify anexisting, or generate a new, EBP for a target entity. In turn, therequest is processed in step 2106 to retrieve relevant entity and APdata associated with the entity, as described in greater detail herein.

A determination is then made in step 2108 whether the request receivedin step 2104 is to modify an existing EBP associated with the targetentity. If so, then the request is processed in step 2110 with theretrieved entity and EBP data, and the existing EBP, to determine whichmodifications are to be made. A determination is then made in step 2112whether to use one or more other prepopulated EBPs relevant to makingthe modifications. If so, the request, the retrieved entity and EBPdata, and the existing EBP is processed in step 2114 to identify otherrelevant prepopulated EBPs that may be used to make the modifications.

Once identified, the identified prepopulated EBPs are retrieved in step2216 and then processed in step 2218 with the request, the retrievedentity and EBP data, and the existing EBP, to generate a modified EBP.The resulting modified EBP is then associated with the entity in step2148, followed by a determination being made in step 2150 whether tocontinue operations to modify an existing, or generating a new, EBP. Ifnot, then the process is continued, proceeding with step 2104.Otherwise, operations to modify an existing, or generating a new, EBPare ended in step 2152.

However, if it was determined in step 2108 not to modify an existing EBPassociated with an entity, then a determination is made in step 2122whether to use a non-prepopulated EBP to generate a new EBP for thetarget entity. If so, the request, the retrieved entity and EBP data,and a target non-prepopulated EBP are processed in step 2124 to generatea new EBP for the target entity. The process is then continued,proceeding with step 2148.

However, if it was determined in step 2122 not to use a non-prepopulatedEBP, then a determination is made in step 2126 whether to use auniversal prepopulated EBP to generate a new EBP for the target entity.If so, the request, the retrieved entity and EBP data, and a targetuniversal prepopulated EBP are processed in step 2128 to generate a newEBP for the target entity. The process is then continued, proceedingwith step 2148.

However, if it was determined in step 2126 not to use a universalprepopulated EBP, then a determination is made in step 2130 whether touse a segmented prepopulated EBP to generate a new EBP for the targetentity. If so, the request, along with the retrieved entity and EBPdata, is then processed in step 2132 to identify relevant segments ofone or more segmented prepopulated EBPs. The request, the retrievedentity and EBP data, and the identified relevant EBP segments are thenprocessed in step 2134 to generate a new EBP for the target entity. Theprocess is then continued, proceeding with step 2148.

However, if it was determined in step 2130 not to use a segmentedprepopulated EBP, then a determination is made in step 2136 whether touse an entity-pertinent prepopulated EBP to generate a new EBP for thetarget entity. If so, the request, along with the retrieved entity andEBP data, is processed in step 2138 to identify one or moreentity-pertinent prepopulated EBPs. The request, along with theretrieved entity and EBP data, and the one or more identifiedentity-pertinent prepopulated EBPs are processed in step 2140 togenerate a new EBP for the target entity. The process is then continued,proceeding with step 2148.

However, if it was determined in step 2136 not to use anentity-pertinent prepopulated EBP, then a determination is made in step2142 whether to use an entity-specific prepopulated EBP to generate anew EBP for the target entity. If so, the request, along with theretrieved entity and EBP data, is processed in step 2144 to identify oneor more entity-specific prepopulated EBPs. The request, along with theretrieved entity and EBP data, and the one or more identifiedentity-specific prepopulated EBPs are processed in step 2146 to generatea new EBP for the target entity. The process is then continued,proceeding with step 2148.

FIGS. 22a through 22d are a generalized flowchart of entity behaviorprofile (EBP) system operations performed in accordance with anembodiment of the invention to generate a new, or modify an existing,prepopulated EBP. In this embodiment, prepopulated EBP modification andgeneration operations are begun in step 2202, followed by the receipt ofa request in step 2204 to perform certain EBP operations to modify anexisting, or generate a new, prepopulated EBP. In turn, the request isprocessed in step 2206 to retrieve relevant entity and EBP data, asdescribed in greater detail herein.

A determination is then made in step 2208 whether the request receivedin step 2204 is to modify an existing prepopulated EBP. If so, then therequest is processed in step 2210 with the retrieved entity and EBPdata, and the existing prepopulated EBP, to determine whichmodifications are to be made. A determination is then made in step 2212whether to use one or more other prepopulated EBPs relevant to makingthe modifications. If so, the request, the retrieved entity and EBPdata, and the existing prepopulated EBP is processed in step 2214 toidentify other relevant prepopulated EBPs that may be used to make themodifications.

Once identified, the identified prepopulated EBPs are retrieved in step2216 and then processed in step 2218 with the request, the retrievedentity and EBP data, and the existing prepopulated EBP, to generate amodified prepopulated EBP. The resulting prepopulated EBP is then storedin a repository of prepopulated EBPs in step 2246, followed by adetermination being made in step 2248 whether to continue operations tomodify an existing, or generating a new, prepopulated EBP. If not, thenthe process is continued, proceeding with step 2204.

Otherwise, operations to modify an existing, or generating a new,prepopulated EBP are ended in step 2250. However, if it was determinedin step 2212 not to use one or more other prepopulated EBPs relevant tomaking the modifications, then the request, the retrieved entity and EBPdata, and the existing prepopulated EBP are processed in step 2220 togenerate a modified prepopulated EBP. The process is then continued,proceeding with step 2246.

However, if it was determined in step 2208 not to modify an existingprepopulated EBP, then a determination is made in step 2222 whether touse a universal prepopulated EBP to generate a new prepopulated EBP. Ifso, the request, the retrieved entity and EBP data, and a targetuniversal prepopulated EBP are processed in step 2224 to generate a newprepopulated EBP. The process is then continued, proceeding with step2246.

However, if it was determined in step 2222 not to use a universalprepopulated EBP, then a determination is made in step 2226 whether touse a segmented prepopulated EBP to generate a new prepopulated EBP. Ifso, the request, along with the retrieved entity and EBP data, is thenprocessed in step 2228 to determine relevant segments of one or moresegmented prepopulated EBPs. The request, the retrieved entity and EBPdata, and the identified relevant EBP segments are then processed instep 2232 to generate a new prepopulated EBP. The process is thencontinued, proceeding with step 2246.

However, if it was determined in step 2226 not to use a segmentedprepopulated EBP, then a determination is made in step 2234 whether touse an entity-pertinent prepopulated EBP to generate a new prepopulatedEBP. If so, the request, along with the retrieved entity and EBP data,is processed in step 2236 to identify one or more entity-pertinentprepopulated EBPs. The request, along with the retrieved entity and EBPdata, and the one or more identified entity-pertinent prepopulated EBPsare processed in step 2238 to generate a new EBP for the target entity.The process is then continued, proceeding with step 2246.

However, if it was determined in step 2234 not to use anentity-pertinent prepopulated EBP, then a determination is made in step2240 whether to use an entity-specific prepopulated EBP to generate anew prepopulated EBP. If so, the request, along with the retrievedentity and EBP data, is processed in step 2244 to identify one or moreentity-specific prepopulated EBPs. The request, along with the retrievedentity and EBP data, and the one or more identified entity-specific EBPsare processed in step 2244 to generate a new prepopulated EBP. Theprocess is then continued, proceeding with step 2246.

FIG. 23 is generalized process flow 2300 for detecting entity behaviorand triggering indicator of behaviors. The order in which the processflow is described is not intended to be construed as a limitation, andany number of the described blocks can be combined in any order toimplement the process flow. Additionally, individual blocks may bedeleted from the process flow without departing from the spirit andscope of the subject matter described herein. Furthermore, the processflow may be implemented in any suitable hardware, software, firmware, ora combination thereof, without departing from the scope of theinvention.

At step 2302, the process 2300 starts. At step 2304, data is receivedfrom various sources as to entity actions. The various sources caninclude network access, social networks, websites, cloud access, etc. Atstep 2306, the data input is monitored over particular time periods.Entity actions are monitored at particular time periods. At step 2308,based on the monitored data input/entity actions, a detectionprobability is determined at the time periods. The detection probabilitycan be increase as time progresses. At step 2310, a trigger indicator ofbehavior is provided if the detection probability reaches a thresholdvalue. The trigger indicator of behavior can be sent to a endpointdevice.

As will be appreciated by one skilled in the art, the present inventionmay be embodied as a method, system, or computer program product.Accordingly, embodiments of the invention may be implemented entirely inhardware, entirely in software (including firmware, resident software,micro-code, etc.) or in an embodiment combining software and hardware.These various embodiments may all generally be referred to herein as a“circuit,” “module,” or “system.” Furthermore, the present invention maytake the form of a computer program product on a computer-usable storagemedium having computer-usable program code embodied in the medium.

Any suitable computer usable or computer readable medium may beutilized. The computer-usable or computer-readable medium may be, forexample, but not limited to, an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor system, apparatus, ordevice. More specific examples (a non-exhaustive list) of thecomputer-readable medium would include the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a portable compact disc read-only memory (CD-ROM), anoptical storage device, or a magnetic storage device. In the context ofthis document, a computer-usable or computer-readable medium may be anymedium that can contain, store, communicate, or transport the programfor use by or in connection with the instruction execution system,apparatus, or device.

Computer program code for carrying out operations of the presentinvention may be written in an object oriented programming language suchas Java, Smalltalk, C++ or the like. However, the computer program codefor carrying out operations of the present invention may also be writtenin conventional procedural programming languages, such as the “C”programming language or similar programming languages. The program codemay execute entirely on the user's computer, partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Embodiments of the invention are described with reference to flowchartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products according to embodiments of the invention. Itwill be understood that each block of the flowchart illustrations and/orblock diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, can be implemented by computerprogram instructions. These computer program instructions may beprovided to a processor of a general purpose computer, special purposecomputer, or other programmable data processing apparatus to produce amachine, such that the instructions, which execute via the processor ofthe computer or other programmable data processing apparatus, createmeans for implementing the functions/acts specified in the flowchartand/or block diagram block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

While particular embodiments of the present invention have been shownand described, it will be obvious to those skilled in the art that,based upon the teachings herein, changes and modifications may be madewithout departing from this invention and its broader aspects.Therefore, the appended claims are to encompass within their scope allsuch changes and modifications as are within the true spirit and scopeof this invention. Furthermore, it is to be understood that theinvention is solely defined by the appended claims. It will beunderstood by those with skill in the art that if a specific number ofan introduced claim element is intended, such intent will be explicitlyrecited in the claim, and in the absence of such recitation no suchlimitation is present. For non-limiting example, as an aid tounderstanding, the following appended claims contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimelements. However, the use of such phrases should not be construed toimply that the introduction of a claim element by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim element to inventions containing only one such element,even when the same claim includes the introductory phrases “one or more”or “at least one” and indefinite articles such as “a” or “an”; the sameholds true for the use in the claims of definite articles.

The present invention is well adapted to attain the advantages mentionedas well as others inherent therein. While the present invention has beendepicted, described, and is defined by reference to particularembodiments of the invention, such references do not imply a limitationon the invention, and no such limitation is to be inferred. Theinvention is capable of considerable modification, alteration, andequivalents in form and function, as will occur to those ordinarilyskilled in the pertinent arts. The depicted and described embodimentsare examples only, and are not exhaustive of the scope of the invention.

Consequently, the invention is intended to be limited only by the spiritand scope of the appended claims, giving full cognizance to equivalentsin all respects.

What is claimed is:
 1. A computer-implementable method for detectingentity behavior, comprising: receiving structured or unstructured rawdata input from various sources related to actions of an entity;monitoring over time, the raw data input and associating the actions ofthe entity to particular time periods; determining a detectionprobability per each time period as to a triggering an indicator ofbehavior regarding a malicious activity, wherein the detectionprobability increases over time; and providing a trigger indicator ofbehavior if the detection probability reaches a threshold value.
 2. Themethod of claim 1, wherein the receiving is agnostic as to the varioussources.
 3. The method of claim 1 further comprising performing one ormore of: querying the raw data and writing observables, querying the rawdata and writing summary data; querying summary data and writingobservables; and querying observable, enrichment, and entity state towrite detection.
 4. The method of claim 1, wherein the monitoringfurther comprises determining entity behavior patterns.
 5. The method ofclaim 1 further comprising determining entity behavior patterns based onphase stacking.
 6. The method of claim 1, wherein the providing atrigger indicator of behavior comprises sending dynamic risk signals asto dynamic actions, dynamic data protection, and dynamic userprotection.
 7. A system comprising: a processor; a data bus coupled tothe processor; and a non-transitory, computer-readable storage mediumembodying computer program code, the non-transitory, computer-readablestorage medium being coupled to the data bus, the computer program codeinteracting with a plurality of computer operations and comprisinginstructions executable by the processor and configured for: receivingstructured or unstructured raw data input from various sources relatedto actions of an entity; monitoring over time, the raw data input andassociating the actions of the entity to particular time periods;determining a detection probability per each time period as to atriggering an indicator of behavior regarding a malicious activity,wherein the detection probability increases over time; and providing atrigger indicator of behavior if the detection probability reaches athreshold value.
 8. The system of claim 7, wherein the receiving isagnostic as to the various sources.
 9. The system of claim 7, furthercomprising performing one or more of: querying the raw data and writingobservables, querying the raw data and writing summary data; queryingsummary data and writing observables; and querying observable,enrichment, and entity state to write detection.
 10. The system of claim7, wherein the monitoring further comprises determining entity behaviorpatterns.
 11. The system of claim 7 further comprising determiningentity behavior patterns based on phase stacking.
 12. The system ofclaim 11, wherein the providing a trigger indicator of behaviorcomprises sending dynamic risk signals as to dynamic actions, dynamicdata protection, and dynamic user protection.
 13. A non-transitory,computer-readable storage medium embodying computer program code, thecomputer program code comprising computer executable instructionsconfigured for: receiving structured or unstructured raw data input fromvarious sources related to actions of an entity; monitoring over time,the raw data input and associating the actions of the entity toparticular time periods; determining a detection probability per eachtime period as to a triggering an indicator of behavior regarding amalicious activity, wherein the detection probability increases overtime; and providing a trigger indicator of behavior if the detectionprobability reaches a threshold value.
 14. The non-transitory,computer-readable storage medium of claim 13, wherein the receiving isagnostic as to the various sources.
 15. The non-transitory,computer-readable storage medium of claim 13 further comprisingperforming one or more of: querying the raw data and writingobservables, querying the raw data and writing summary data; queryingsummary data and writing observables; and querying observable,enrichment, and entity state to write detection.
 16. The non-transitory,computer-readable storage medium of claim 13, wherein the monitoringfurther comprises determining entity behavior patterns.
 17. Thenon-transitory, computer-readable storage medium of claim 13 furthercomprising determining entity behavior patterns based on phase stacking.18. The non-transitory, computer-readable storage medium of claim 13,wherein the providing a trigger indicator of behavior comprises sendingdynamic risk signals as to dynamic actions, dynamic data protection, anddynamic user protection.
 19. The non-transitory, computer-readablestorage medium of claim 13, wherein: the computer executableinstructions are deployable to a client system from a server system at aremote location.
 20. The non-transitory, computer-readable storagemedium of claim 13, wherein: the computer executable instructions areprovided by a service provider to a user on an on-demand basis.